phd machine learning topics

Exploring 250+ Machine Learning Research Topics

In recent years, machine learning has become super popular and grown very quickly. This happened because technology got better, and there’s a lot more data available. Because of this, we’ve seen lots of new and amazing things happen in different areas. Machine learning research is what makes all these cool things possible. In this blog, we’ll talk about machine learning research topics, why they’re important, how you can pick one, what areas are popular to study, what’s new and exciting, the tough problems, and where you can find help if you want to be a researcher.

Why Does Machine Learning Research Matter?

Table of Contents

Machine learning research is at the heart of the AI revolution. It underpins the development of intelligent systems capable of making predictions, automating tasks, and improving decision-making across industries. The importance of this research can be summarized as follows:

Advancements in Technology

The growth of machine learning research has led to the development of powerful algorithms, tools, and frameworks. Numerous industries, including healthcare, banking, autonomous cars, and natural language processing, have found use for these technology.

As researchers continue to push the boundaries of what’s possible, we can expect even more transformative technologies to emerge.

Real-world Applications

Machine learning research has brought about tangible changes in our daily lives. Voice assistants like Siri and Alexa, recommendation systems on streaming platforms, and personalized healthcare diagnostics are just a few examples of how this research impacts our world. 

By working on new research topics, scientists can further refine these applications and create new ones.

Economic and Industrial Impacts

The economic implications of machine learning research are substantial. Companies that harness the power of machine learning gain a competitive edge in the market. 

This creates a demand for skilled machine learning researchers, driving job opportunities and contributing to economic growth.

How to Choose the Machine Learning Research Topics?

Selecting the right machine learning research topics is crucial for your success as a machine learning researcher. Here’s a guide to help you make an informed decision:

• Understanding Your Interests

Start by considering your personal interests. Machine learning is a broad field with applications in virtually every sector. By choosing a topic that aligns with your passions, you’ll stay motivated and engaged throughout your research journey.

• Reviewing Current Trends

Stay updated on the latest trends in machine learning. Attend conferences, read research papers, and engage with the community to identify emerging research topics. Current trends often lead to exciting breakthroughs.

• Identifying Gaps in Existing Research

Sometimes, the most promising research topics involve addressing gaps in existing knowledge. These gaps may become evident through your own experiences, discussions with peers, or in the course of your studies.

• Collaborating with Experts

Collaboration is key in research. Working with experts in the field can help you refine your research topic and gain valuable insights. Seek mentors and collaborators who can guide you.

250+ Machine Learning Research Topics: Category-wise

Supervised learning.

• Explainable AI for Decision Support
• Few-shot Learning Methods
• Time Series Forecasting with Deep Learning
• Handling Imbalanced Datasets in Classification
• Regression Techniques for Non-linear Data
• Transfer Learning in Supervised Settings
• Multi-label Classification Strategies
• Semi-Supervised Learning Approaches
• Novel Feature Selection Methods
• Anomaly Detection in Supervised Scenarios
• Federated Learning for Distributed Supervised Models
• Ensemble Learning for Improved Accuracy
• Automated Hyperparameter Tuning
• Ethical Implications in Supervised Models
• Interpretability of Deep Neural Networks.

Unsupervised Learning

• Unsupervised Clustering of High-dimensional Data
• Semi-Supervised Clustering Approaches
• Density Estimation in Unsupervised Learning
• Anomaly Detection in Unsupervised Settings
• Transfer Learning for Unsupervised Tasks
• Representation Learning in Unsupervised Learning
• Outlier Detection Techniques
• Generative Models for Data Synthesis
• Manifold Learning in High-dimensional Spaces
• Unsupervised Feature Selection
• Privacy-Preserving Unsupervised Learning
• Community Detection in Complex Networks
• Clustering Interpretability and Visualization
• Unsupervised Learning for Image Segmentation
• Autoencoders for Dimensionality Reduction.

Reinforcement Learning

• Deep Reinforcement Learning in Real-world Applications
• Safe Reinforcement Learning for Autonomous Systems
• Transfer Learning in Reinforcement Learning
• Imitation Learning and Apprenticeship Learning
• Multi-agent Reinforcement Learning
• Explainable Reinforcement Learning Policies
• Hierarchical Reinforcement Learning
• Model-based Reinforcement Learning
• Curriculum Learning in Reinforcement Learning
• Reinforcement Learning in Robotics
• Exploration vs. Exploitation Strategies
• Reward Function Design and Ethical Considerations
• Reinforcement Learning in Healthcare
• Continuous Action Spaces in RL
• Reinforcement Learning for Resource Management.

Natural Language Processing (NLP)

• Multilingual and Cross-lingual NLP
• Contextualized Word Embeddings
• Bias Detection and Mitigation in NLP
• Named Entity Recognition for Low-resource Languages
• Sentiment Analysis in Social Media Text
• Dialogue Systems for Improved Customer Service
• Text Summarization for News Articles
• Low-resource Machine Translation
• Explainable NLP Models
• Coreference Resolution in NLP
• Question Answering in Specific Domains
• Detecting Fake News and Misinformation
• NLP for Healthcare: Clinical Document Understanding
• Emotion Analysis in Text
• Text Generation with Controlled Attributes.

Computer Vision

• Video Action Recognition and Event Detection
• Object Detection in Challenging Conditions (e.g., low light)
• Explainable Computer Vision Models
• Image Captioning for Accessibility
• Large-scale Image Retrieval
• Domain Adaptation in Computer Vision
• Fine-grained Image Classification
• Facial Expression Recognition
• Visual Question Answering
• Self-supervised Learning for Visual Representations
• Weakly Supervised Object Localization
• Human Pose Estimation in 3D
• Scene Understanding in Autonomous Vehicles
• Image Super-resolution
• Gaze Estimation for Human-Computer Interaction.

Deep Learning

• Neural Architecture Search for Efficient Models
• Self-attention Mechanisms and Transformers
• Interpretability in Deep Learning Models
• Robustness of Deep Neural Networks
• Generative Adversarial Networks (GANs) for Data Augmentation
• Neural Style Transfer in Art and Design
• Adversarial Attacks and Defenses
• Neural Networks for Audio and Speech Processing
• Explainable AI for Healthcare Diagnosis
• Automated Machine Learning (AutoML)
• Reinforcement Learning with Deep Neural Networks
• Model Compression and Quantization
• Lifelong Learning with Deep Learning Models
• Multimodal Learning with Vision and Language
• Federated Learning for Privacy-preserving Deep Learning.

Explainable AI

• Visualizing Model Decision Boundaries
• Saliency Maps and Feature Attribution
• Rule-based Explanations for Black-box Models
• Contrastive Explanations for Model Interpretability
• Counterfactual Explanations and What-if Analysis
• Human-centered AI for Explainable Healthcare
• Ethics and Fairness in Explainable AI
• Explanation Generation for Natural Language Processing
• Explainable AI in Financial Risk Assessment
• User-friendly Interfaces for Model Interpretability
• Scalability and Efficiency in Explainable Models
• Hybrid Models for Combined Accuracy and Explainability
• Post-hoc vs. Intrinsic Explanations
• Evaluation Metrics for Explanation Quality
• Explainable AI for Autonomous Vehicles.

Transfer Learning

• Zero-shot Learning and Few-shot Learning
• Cross-domain Transfer Learning
• Domain Adaptation for Improved Generalization
• Multilingual Transfer Learning in NLP
• Pretraining and Fine-tuning Techniques
• Lifelong Learning and Continual Learning
• Domain-specific Transfer Learning Applications
• Model Distillation for Knowledge Transfer
• Contrastive Learning for Transfer Learning
• Self-training and Pseudo-labeling
• Dynamic Adaption of Pretrained Models
• Privacy-Preserving Transfer Learning
• Unsupervised Domain Adaptation
• Negative Transfer Avoidance in Transfer Learning.

Federated Learning

• Secure Aggregation in Federated Learning
• Communication-efficient Federated Learning
• Privacy-preserving Techniques in Federated Learning
• Federated Transfer Learning
• Heterogeneous Federated Learning
• Real-world Applications of Federated Learning
• Federated Learning for Edge Devices
• Federated Learning for Healthcare Data
• Differential Privacy in Federated Learning
• Byzantine-robust Federated Learning
• Federated Learning with Non-IID Data
• Model Selection in Federated Learning
• Scalable Federated Learning for Large Datasets
• Client Selection and Sampling Strategies
• Global Model Update Synchronization in Federated Learning.

Quantum Machine Learning

• Quantum Neural Networks and Quantum Circuit Learning
• Quantum-enhanced Optimization for Machine Learning
• Quantum Data Compression and Quantum Principal Component Analysis
• Quantum Kernels and Quantum Feature Maps
• Quantum Variational Autoencoders
• Quantum Transfer Learning
• Quantum-inspired Classical Algorithms for ML
• Hybrid Quantum-Classical Models
• Quantum Machine Learning on Near-term Quantum Devices
• Quantum-inspired Reinforcement Learning
• Quantum Computing for Quantum Chemistry and Drug Discovery
• Quantum Machine Learning for Finance
• Quantum Data Structures and Quantum Databases
• Quantum-enhanced Cryptography in Machine Learning
• Quantum Generative Models and Quantum GANs.

Ethical AI and Bias Mitigation

• Fairness-aware Machine Learning Algorithms
• Bias Detection and Mitigation in Real-world Data
• Explainable AI for Ethical Decision Support
• Algorithmic Accountability and Transparency
• Privacy-preserving AI and Data Governance
• Ethical Considerations in AI for Healthcare
• Fairness in Recommender Systems
• Bias and Fairness in NLP Models
• Auditing AI Systems for Bias
• Societal Implications of AI in Criminal Justice
• Ethical AI Education and Training
• Bias Mitigation in Autonomous Vehicles
• Fair AI in Financial and Hiring Decisions
• Case Studies in Ethical AI Failures
• Legal and Policy Frameworks for Ethical AI.

Meta-Learning and AutoML

• Neural Architecture Search (NAS) for Efficient Models
• Transfer Learning in NAS
• Reinforcement Learning for NAS
• Multi-objective NAS
• Automated Data Augmentation
• Neural Architecture Optimization for Edge Devices
• Bayesian Optimization for AutoML
• Model Compression and Quantization in AutoML
• AutoML for Federated Learning
• AutoML in Healthcare Diagnostics
• Explainable AutoML
• Cost-sensitive Learning in AutoML
• AutoML for Small Data
• Human-in-the-Loop AutoML.

AI for Healthcare and Medicine

• Disease Prediction and Early Diagnosis
• Medical Image Analysis with Deep Learning
• Drug Discovery and Molecular Modeling
• Electronic Health Record Analysis
• Predictive Analytics in Healthcare
• Personalized Treatment Planning
• Healthcare Fraud Detection
• Telemedicine and Remote Patient Monitoring
• AI in Radiology and Pathology
• AI in Drug Repurposing
• AI for Medical Robotics and Surgery
• Genomic Data Analysis
• AI-powered Mental Health Assessment
• Explainable AI in Healthcare Decision Support
• AI in Epidemiology and Outbreak Prediction.

AI in Finance and Investment

• Algorithmic Trading and High-frequency Trading
• Credit Scoring and Risk Assessment
• Fraud Detection and Anti-money Laundering
• Portfolio Optimization with AI
• Financial Market Prediction
• Sentiment Analysis in Financial News
• Explainable AI in Financial Decision-making
• Algorithmic Pricing and Dynamic Pricing Strategies
• AI in Cryptocurrency and Blockchain
• Customer Behavior Analysis in Banking
• Explainable AI in Credit Decisioning
• AI in Regulatory Compliance
• Ethical AI in Financial Services
• AI for Real Estate Investment
• Automated Financial Reporting.

AI in Climate Change and Sustainability

• Climate Modeling and Prediction
• Renewable Energy Forecasting
• Smart Grid Optimization
• Energy Consumption Forecasting
• Carbon Emission Reduction with AI
• Ecosystem Monitoring and Preservation
• Precision Agriculture with AI
• AI for Wildlife Conservation
• Natural Disaster Prediction and Management
• Water Resource Management with AI
• Sustainable Transportation and Urban Planning
• Climate Change Mitigation Strategies with AI
• Environmental Impact Assessment with Machine Learning
• Eco-friendly Supply Chain Optimization
• Ethical AI in Climate-related Decision Support.

Data Privacy and Security

• Differential Privacy Mechanisms
• Federated Learning for Privacy-preserving AI
• Secure Multi-Party Computation
• Privacy-enhancing Technologies in Machine Learning
• Homomorphic Encryption for Machine Learning
• Ethical Considerations in Data Privacy
• Privacy-preserving AI in Healthcare
• AI for Secure Authentication and Access Control
• Blockchain and AI for Data Security
• Explainable Privacy in Machine Learning
• Privacy-preserving AI in Government and Public Services
• Privacy-compliant AI for IoT and Edge Devices
• Secure AI Models Sharing and Deployment
• Privacy-preserving AI in Financial Transactions
• AI in the Legal Frameworks of Data Privacy.

Global Collaboration in Research

• International Research Partnerships and Collaboration Models
• Multilingual and Cross-cultural AI Research
• Addressing Global Healthcare Challenges with AI
• Ethical Considerations in International AI Collaborations
• Interdisciplinary AI Research in Global Challenges
• AI Ethics and Human Rights in Global Research
• Data Sharing and Data Access in Global AI Research
• Cross-border Research Regulations and Compliance
• AI Innovation Hubs and International Research Centers
• AI Education and Training for Global Communities
• Humanitarian AI and AI for Sustainable Development Goals
• AI for Cultural Preservation and Heritage Protection
• Collaboration in AI-related Global Crises
• AI in Cross-cultural Communication and Understanding
• Global AI for Environmental Sustainability and Conservation.

Emerging Trends and Hot Topics in Machine Learning Research

The landscape of machine learning research topics is constantly evolving. Here are some of the emerging trends and hot topics that are shaping the field:

As AI systems become more prevalent, addressing ethical concerns and mitigating bias in algorithms are critical research areas.

Interpretable and Explainable Models

Understanding why machine learning models make specific decisions is crucial for their adoption in sensitive areas, such as healthcare and finance.

Meta-learning algorithms are designed to enable machines to learn how to learn, while AutoML aims to automate the machine learning process itself.

Machine learning is revolutionizing the healthcare sector, from diagnostic tools to drug discovery and patient care.

Algorithmic trading, risk assessment, and fraud detection are just a few applications of AI in finance, creating a wealth of research opportunities.

Machine learning research is crucial in analyzing and mitigating the impacts of climate change and promoting sustainable practices.

Challenges and Future Directions

While machine learning research has made tremendous strides, it also faces several challenges:

• Data Privacy and Security: As machine learning models require vast amounts of data, protecting individual privacy and data security are paramount concerns.
• Scalability and Efficiency: Developing efficient algorithms that can handle increasingly large datasets and complex computations remains a challenge.
• Ensuring Fairness and Transparency: Addressing bias in machine learning models and making their decisions transparent is essential for equitable AI systems.
• Quantum Computing and Machine Learning: The integration of quantum computing and machine learning has the potential to revolutionize the field, but it also presents unique challenges.
• Global Collaboration in Research: Machine learning research benefits from collaboration on a global scale. Ensuring that researchers from diverse backgrounds work together is vital for progress.

Resources for Machine Learning Researchers

If you’re looking to embark on a journey in machine learning research topics, there are various resources at your disposal:

• Journals and Conferences

Journals such as the “Journal of Machine Learning Research” and conferences like NeurIPS and ICML provide a platform for publishing and discussing research findings.

• Online Communities and Forums

Platforms like Stack Overflow, GitHub, and dedicated forums for machine learning provide spaces for collaboration and problem-solving.

• Datasets and Tools

Open-source datasets and tools like TensorFlow and PyTorch simplify the research process by providing access to data and pre-built models.

• Research Grants and Funding Opportunities

Many organizations and government agencies offer research grants and funding for machine learning projects. Seek out these opportunities to support your research.

Machine learning research is like a superhero in the world of technology. To be a part of this exciting journey, it’s important to choose the right machine learning research topics and keep up with the latest trends.

Machine learning research makes our lives better. It powers things like smart assistants and life-saving medical tools. It’s like the force driving the future of technology and society.

But, there are challenges too. We need to work together and be ethical in our research. Everyone should benefit from this technology. The future of machine learning research is incredibly bright. If you want to be a part of it, get ready for an exciting adventure. You can help create new solutions and make a big impact on the world.

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Research Topics & Ideas

Artifical Intelligence (AI) and Machine Learning (ML)

If you’re just starting out exploring AI-related research topics for your dissertation, thesis or research project, you’ve come to the right place. In this post, we’ll help kickstart your research topic ideation process by providing a hearty list of research topics and ideas , including examples from past studies.

PS – This is just the start…

We know it’s exciting to run through a list of research topics, but please keep in mind that this list is just a starting point . To develop a suitable research topic, you’ll need to identify a clear and convincing research gap , and a viable plan  to fill that gap.

If this sounds foreign to you, check out our free research topic webinar that explores how to find and refine a high-quality research topic, from scratch. Alternatively, if you’d like hands-on help, consider our 1-on-1 coaching service .

AI-Related Research Topics & Ideas

Below you’ll find a list of AI and machine learning-related research topics ideas. These are intentionally broad and generic , so keep in mind that you will need to refine them a little. Nevertheless, they should inspire some ideas for your project.

• Developing AI algorithms for early detection of chronic diseases using patient data.
• The use of deep learning in enhancing the accuracy of weather prediction models.
• Machine learning techniques for real-time language translation in social media platforms.
• AI-driven approaches to improve cybersecurity in financial transactions.
• The role of AI in optimizing supply chain logistics for e-commerce.
• Investigating the impact of machine learning in personalized education systems.
• The use of AI in predictive maintenance for industrial machinery.
• Developing ethical frameworks for AI decision-making in healthcare.
• The application of ML algorithms in autonomous vehicle navigation systems.
• AI in agricultural technology: Optimizing crop yield predictions.
• Machine learning techniques for enhancing image recognition in security systems.
• AI-powered chatbots: Improving customer service efficiency in retail.
• The impact of AI on enhancing energy efficiency in smart buildings.
• Deep learning in drug discovery and pharmaceutical research.
• The use of AI in detecting and combating online misinformation.
• Machine learning models for real-time traffic prediction and management.
• AI applications in facial recognition: Privacy and ethical considerations.
• The effectiveness of ML in financial market prediction and analysis.
• Developing AI tools for real-time monitoring of environmental pollution.
• Machine learning for automated content moderation on social platforms.
• The role of AI in enhancing the accuracy of medical diagnostics.
• AI in space exploration: Automated data analysis and interpretation.
• Machine learning techniques in identifying genetic markers for diseases.
• AI-driven personal finance management tools.
• The use of AI in developing adaptive learning technologies for disabled students.

AI & ML Research Topic Ideas (Continued)

• Machine learning in cybersecurity threat detection and response.
• AI applications in virtual reality and augmented reality experiences.
• Developing ethical AI systems for recruitment and hiring processes.
• Machine learning for sentiment analysis in customer feedback.
• AI in sports analytics for performance enhancement and injury prevention.
• The role of AI in improving urban planning and smart city initiatives.
• Machine learning models for predicting consumer behaviour trends.
• AI and ML in artistic creation: Music, visual arts, and literature.
• The use of AI in automated drone navigation for delivery services.
• Developing AI algorithms for effective waste management and recycling.
• Machine learning in seismology for earthquake prediction.
• AI-powered tools for enhancing online privacy and data protection.
• The application of ML in enhancing speech recognition technologies.
• Investigating the role of AI in mental health assessment and therapy.
• Machine learning for optimization of renewable energy systems.
• AI in fashion: Predicting trends and personalizing customer experiences.
• The impact of AI on legal research and case analysis.
• Developing AI systems for real-time language interpretation for the deaf and hard of hearing.
• Machine learning in genomic data analysis for personalized medicine.
• AI-driven algorithms for credit scoring in microfinance.
• The use of AI in enhancing public safety and emergency response systems.
• Machine learning for improving water quality monitoring and management.
• AI applications in wildlife conservation and habitat monitoring.
• The role of AI in streamlining manufacturing processes.
• Investigating the use of AI in enhancing the accessibility of digital content for visually impaired users.

Recent AI & ML-Related Studies

While the ideas we’ve presented above are a decent starting point for finding a research topic in AI, they are fairly generic and non-specific. So, it helps to look at actual studies in the AI and machine learning space to see how this all comes together in practice.

Below, we’ve included a selection of AI-related studies to help refine your thinking. These are actual studies,  so they can provide some useful insight as to what a research topic looks like in practice.

• An overview of artificial intelligence in diabetic retinopathy and other ocular diseases (Sheng et al., 2022)
• HOW DOES ARTIFICIAL INTELLIGENCE HELP ASTRONOMY? A REVIEW (Patel, 2022)
• Editorial: Artificial Intelligence in Bioinformatics and Drug Repurposing: Methods and Applications (Zheng et al., 2022)
• Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities, and Challenges (Mukhamediev et al., 2022)
• Will digitization, big data, and artificial intelligence – and deep learning–based algorithm govern the practice of medicine? (Goh, 2022)
• Flower Classifier Web App Using Ml & Flask Web Framework (Singh et al., 2022)
• Object-based Classification of Natural Scenes Using Machine Learning Methods (Jasim & Younis, 2023)
• Automated Training Data Construction using Measurements for High-Level Learning-Based FPGA Power Modeling (Richa et al., 2022)
• Artificial Intelligence (AI) and Internet of Medical Things (IoMT) Assisted Biomedical Systems for Intelligent Healthcare (Manickam et al., 2022)
• Critical Review of Air Quality Prediction using Machine Learning Techniques (Sharma et al., 2022)
• Artificial Intelligence: New Frontiers in Real–Time Inverse Scattering and Electromagnetic Imaging (Salucci et al., 2022)
• Machine learning alternative to systems biology should not solely depend on data (Yeo & Selvarajoo, 2022)
• Measurement-While-Drilling Based Estimation of Dynamic Penetrometer Values Using Decision Trees and Random Forests (García et al., 2022).
• Artificial Intelligence in the Diagnosis of Oral Diseases: Applications and Pitfalls (Patil et al., 2022).
• Automated Machine Learning on High Dimensional Big Data for Prediction Tasks (Jayanthi & Devi, 2022)
• Breakdown of Machine Learning Algorithms (Meena & Sehrawat, 2022)
• Technology-Enabled, Evidence-Driven, and Patient-Centered: The Way Forward for Regulating Software as a Medical Device (Carolan et al., 2021)
• Machine Learning in Tourism (Rugge, 2022)
• Towards a training data model for artificial intelligence in earth observation (Yue et al., 2022)
• Classification of Music Generality using ANN, CNN and RNN-LSTM (Tripathy & Patel, 2022)

As you can see, these research topics are a lot more focused than the generic topic ideas we presented earlier. So, in order for you to develop a high-quality research topic, you’ll need to get specific and laser-focused on a specific context with specific variables of interest.  In the video below, we explore some other important things you’ll need to consider when crafting your research topic.

Get 1-On-1 Help

If you’re still unsure about how to find a quality research topic, check out our Research Topic Kickstarter service, which is the perfect starting point for developing a unique, well-justified research topic.

can one come up with their own tppic and get a search

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PhD Programme in Advanced Machine Learning

The Cambridge Machine Learning Group (MLG) runs a PhD programme in Advanced Machine Learning. The supervisors are Jose Miguel Hernandez-Lobato , Carl Rasmussen , Richard E. Turner , Adrian Weller , Hong Ge and David Krueger . Zoubin Ghahramani is currently on academic leave and not accepting new students at this time.

We encourage applications from outstanding candidates with academic backgrounds in Mathematics, Physics, Computer Science, Engineering and related fields, and a keen interest in doing basic research in machine learning and its scientific applications. There are no additional restrictions on the topic of the PhD, but for further information on our current research areas, please consult our webpages at http://mlg.eng.cam.ac.uk .

The typical duration of the PhD will be four years.

Applicants must formally apply through the Applicant Portal at the University of Cambridge by the deadline, indicating “PhD in Engineering” as the course (supervisor Hernandez-Lobato, Rasmussen, Turner, Weller, Ge and/or Krueger). Applicants who want to apply for University funding need to reply ‘Yes’ to the question ‘Apply for Cambridge Scholarships’. See http://www.admin.cam.ac.uk/students/gradadmissions/prospec/apply/deadlines.html for details. Note that applications will not be complete until all the required material has been uploaded (including reference letters), and we will not be able to see any applications until that happens.

Gates funding applicants (US or other overseas) need to fill out the dedicated Gates Cambridge Scholarships section later on the form which is sent on to the administrators of Gates funding.

Deadline for PhD Application: noon 5 December, 2023

Applications from outstanding individuals may be considered after this time, but applying later may adversely impact your chances for both admission and funding.

FURTHER INFORMATION ABOUT COMPLETING THE ADMISSIONS FORMS:

The Machine Learning Group is based in the Department of Engineering, not Computer Science.

We will assess your application on three criteria:

1 Academic performance (ensure evidence for strong academic achievement, e.g. position in year, awards, etc.) 2 references (clearly your references will need to be strong; they should also mention evidence of excellence as quotes will be drawn from them) 3 research (detail your research experience, especially that which relates to machine learning)

You will also need to put together a research proposal. We do not offer individual support for this. It is part of the application assessment, i.e. ascertaining whether you can write about a research area in a sensible way and pose interesting questions. It is not a commitment to what you will work on during your PhD. Most often PhD topics crystallise over the first year. The research proposal should be about 2 pages long and can be attached to your application (you can indicate that your proposal is attached in the 1500 character count Research Summary box). This aspect of the application does not carry a huge amount of weight so do not spend a large amount of time on it. Please also attach a recent CV to your application too.

INFORMATION ABOUT THE CAMBRIDGE-TUEBINGEN PROGRAMME:

We also offer a small number of PhDs on the Cambridge-Tuebingen programme. This stream is for specific candidates whose research interests are well-matched to both the machine learning group in Cambridge and the MPI for Intelligent Systems in Tuebingen. For more information about the Cambridge-Tuebingen programme and how to apply see here . IMPORTANT: remember to download your application form before you submit so that you can send a copy to the administrators in Tuebingen directly . Note that the application deadline for the Cambridge-Tuebingen programme is noon, 5th December, 2023, CET.

What background do I need?

An ideal background is a top undergraduate or Masters degree in Mathematics, Physics, Computer Science, or Electrical Engineering. You should be both very strong mathematically and have an intuitive and practical grasp of computation. Successful applicants often have research experience in statistical machine learning. Shortlisted applicants are interviewed.

Do you have funding?

There are a number of funding sources at Cambridge University for PhD students, including for international students. All our students receive partial or full funding for the full three years of the PhD. We do not give preference to “self-funded” students. To be eligible for funding it is important to apply early (see https://www.graduate.study.cam.ac.uk/finance/funding – current deadlines are 10 October for US students, and 1 December for others). Also make sure you tick the box on the application saying you wish to be considered for funding!

If you are applying to the Cambridge-Tuebingen programme, note that this source of funding will not be listed as one of the official funding sources, but if you apply to this programme, please tick the other possible sources of funding if you want to maximise your chances of getting funding from Cambridge.

What is my likelihood of being admitted?

Because we receive so many applications, unfortunately we can’t admit many excellent candidates, even some who have funding. Successful applicants tend to be among the very top students at their institution, have very strong mathematics backgrounds, and references, and have some research experience in statistical machine learning.

Do I have to contact one of the faculty members first or can I apply formally directly?

It is not necessary, but if you have doubts about whether your background is suitable for the programme, or if you have questions about the group, you are welcome to contact one of the faculty members directly. Due to their high email volume you may not receive an immediate response but they will endeavour to get back to you as quickly as possible. It is important to make your official application to Graduate Admissions at Cambridge before the funding deadlines, even if you don’t hear back from us; otherwise we may not be able to consider you.

Do you take Masters students, or part-time PhD students?

We generally don’t admit students for a part-time PhD. We also don’t usually admit students just for a pure-research Masters in machine learning , except for specific programs such as the Churchill and Marshall scholarships. However, please do note that we run a one-year taught Master’s Programme: The MPhil in Machine Learning, and Machine Intelligence . You are welcome to apply directly to this.

What Department / course should I indicate on my application form?

This machine learning group is in the Department of Engineering. The degree you would be applying for is a PhD in Engineering (not Computer Science or Statistics).

How long does a PhD take?

A typical PhD from our group takes 3-4 years. The first year requires students to pass some courses and submit a first-year research report. Students must submit their PhD before the 4th year.

What research topics do you have projects on?

We don’t generally pre-specify projects for students. We prefer to find a research area that suits the student. For a sample of our research, you can check group members’ personal pages or our research publications page.

What are the career prospects for PhD students from your group?

Students and postdocs from the group have moved on to excellent positions both in academia and industry. Have a look at our list of recent alumni on the Machine Learning group webpage . Research expertise in machine learning is in very high demand these days.

Machine Learning - CMU

Phd in machine learning.

Core Requirements The curriculum for the Machine Learning Ph.D. is built on a foundation of six core courses and one elective . A typical full-time, PhD student course load during the first two years consists each term of two classes (at 12 graduate units per class) plus 24 units of research. It is expected that all Ph.D. students engage in active research from their first semester.  Roughly half of a student's time should be allocated to research and half to courses until the courses are completed.

Required Core courses:

• 10-715 Advanced Introduction to Machine Learning
• 10-716 Advanced Machine Learning: Theory & Methods
• 36-705 Intermediate Statistics
• 10-718* Machine Learning in Practice *Students who are in the joint PhD program in ML & Statistics may satisfy this requirement through the ADA project in Statistics. Students in the joint ML-CNBC program may satisfy it by completing a data intensive project for their second year milestone. Students in the joint ML-Heinz joint PhD also complete it in their program.

Plus any 2 of the following Menu* of Core courses:

• 10-703 Deep Reinforcement Learning or 10-707 Topics in Deep Learning
• 10-708 Probabilistic Graphical Models
• 10-725 Convex Optimization
• 10-734 Foundations of Autonomous Decision Making under Uncertainty
• 10-805 Machine Learning with Large Datasets
• 15-750 Algorithms in the Real World or 15-850 Advanced Algorithms 
• 15-780 Graduate Artificial Intelligence
• 36-707 Regression Analysis
• 36-709 Advanced Statistical Theory I
• 36-710 Advanced Statistical Theory II

* Students in the Statistics & ML joint program must choose two of the Menu of Core courses with a prefix in a department that is not their home department. Thus, Statistics joint students should choose two 10- and 15- prefix courses, and Machine Learning joint students should choose two 36- and 15- courses.  Students accepted to the Statistics & ML joint program before Spring 2021 are grandfathered and follow the previous rules.

Plus 1 elective:

• An additional course from the Menu Core list above
• Any course at the 700 or higher level in SCS or Statistics (36-xxx)
• Other 700 or higher level courses by approval

Note: Some students will have taken some of the above courses before entering the MLD PhD program: for example, as MS students at CMU. If students have previously taken the above-named courses at Carnegie Mellon before joining the MLD PhD, those may be used to satisfy the requirements and do not need to be repeated. (Note that courses can only be used for a single Master's degree.)

Some students will have taken similar courses at other universities before entering the MLD PhD program. Based on such equivalent coursework, any student can apply to replace (not reduce) up to two courses with either menu cores or electives. All requests must be supported by the advisor, and will be evaluated by the PhD Director. 

Typical schedule of Courses & Milestones

GW Online Engineering Programs

• Artificial Intelligence and Machine Learning
• Cloud Computing Management
• Cybersecurity Analytics
• Cybersecurity Policy & Compliance
• Electrical Engineering
• Engineering Management
• Systems Engineering
• Policies & Procedures
• On-Site International Programs
• Artificial Intelligence & Machine Learning
• Degrees Offered
• Tuition & Financial Aid
• Student Resources
• Application Process

Apply for Admission

Online Doctor of Engineering in Artificial Intelligence & Machine Learning

We are now accepting applications for the cohort beginning in January 2025.

The application deadline is November 1, 2024

Program Description

The online Doctor of Engineering in Artificial Intelligence & Machine Learning is a research-based doctoral program. The program is designed to provide graduates with a solid understanding of the latest AI&ML techniques, as well as hands-on experience in applying these techniques to real-world problems. Graduates of this program are equipped to lead AI&ML projects and teams in a wide range of industries, including healthcare, finance, and manufacturing. Having developed advanced research skills, graduates are also well-prepared for academic research and teaching roles.

The degree requires completion of eight graduate-level courses (listed below) and a minimum of 24 credit hours of Praxis Research (SEAS 8588). During the research phase, the student writes and defends a research praxis on a topic related to AI&ML. The topic is selected by the student and approved by the research advising committee.

SEAS 6414 Python Application for Data Analytics:  Introduction to Python programming tailored for Data Analytics. This course covers Python’s applications in automating data cleaning, feature engineering, outlier detection, implementing machine learning algorithms, conducting text mining, and performing time series analysis. (3 credit hours)

SEAS 8500 Fundamentals of AI-Enabled Systems:  Operational decomposition for AI solutions, engineering data for algorithm development, and deployment strategies. Systems perspective in designing AI systems. Full-lifecycle of creating AI-enabled systems. Ethics and biases in AI systems (3 credit hours)

SEAS 8505 Applied Machine Intelligence and Reinforcement Learning:  Theory and practice of machine learning leveraging open-source tools, algorithms and techniques. Topics include intelligent model training, support vector machines, deep learning, transformer methods, GANs, and reinforcement learning (3 credit hours)

SEAS 8510 Analytical Methods for Machine Learning:  Mathematical tools for building machine learning algorithms: linear algebra, analytical geometry, matrix decompositions, optimization, probability and statistics (3 credit hours)

SEAS 8515 Data Engineering for AI:  Developing Python scripts to automate data pipelines, data ingestion, data processing, and data warehousing. Machine learning applications with Python including text mining and time series analysis (3 credit hours)

SEAS 8520 Deep Learning and Natural Language Processing:  Fundamentals of deep learning and Natural Language Processing (NLP). Techniques for designing modern deep learning networks using Keras and TensorFlow. NLP topics include sentiment analysis, bag of words, TFIDF, and Large Language Models (3 credit hours)

SEAS 8525 Computer Vision and Generative AI: Explore AI's visual realm. Learn image processing object detection, and models in generative adversarial networks and neural networks. Master tools for creating AI applications in art, design, ethical considerations, and societal impacts of generative AI technology (3 credit hours)

SEAS 8599 Praxis Development for AI & Machine Learning:  Overview of research methods. Aims and purpose of the praxis. Development of praxis research strategies, formulation, and defense of a praxis proposal (3 credit hours)

SEAS 8588 Praxis Research for D.Eng. in AI & Machine Learning:  Research leading to the degree of Doctor of Engineering in AI and Machine Learning (24 Credit Hours)  

Classroom courses last 10 weeks each and meet on Saturday mornings from 9:00 AM—12:10 PM and afternoons from 1:00—4:10 PM (all times Eastern). All classes meet live online through synchronous distance learning technologies (Zoom). All classes are recorded and available for viewing within two hours of the lecture. This program is taught in a cohort format in which students take all courses in lockstep. Courses cannot be taken out of sequence, live attendance at all class meetings is expected, and students must remain continuously enrolled. Leaves of absence are permitted only in the case of a medical or family emergency, or deployment to active military duty.  Please see below for the dates of our upcoming cohort.

No classes on Thanksgiving, Christmas, New Year, Fourth of July,  and Memorial Day Weekends 

To proceed to the research phase, students must earn a grade point average of at least 3.2 in the eight classroom courses, and no grade below B-. Students are then registered for a minimum of 24 credit hours of SEAS 8588 Praxis Research: 3 ch in Fall 2025 (Session 2), 9 ch in Spring 2026, 3 ch in Summer 2026, and 9 ch in Fall 2026. Throughout the research phase, students develop the praxis under the guidance of a designated faculty advisor. Faculty research advisors are assigned by the program office and meet individually with students every two weeks.

Sample research areas are listed below:

•    Developing algorithms and methods that can explain how AI systems reach their decisions or predictions, making them more transparent and trustworthy •    Investigating how reinforcement learning can improve robotic performance and control, particularly in complex environments •    Examining how to ensure that AI systems are fair and unbiased in their decision-making, particularly in areas such as hiring, lending, and criminal justice •    Developing more advanced natural language processing models and algorithms that can understand and interpret human language more accurately and effectively •    Investigating how to apply transfer learning techniques to improve the performance of AI systems in new and different domains, with less data and less training time 

Tuition is $1,750 per credit hour for the 2024-2025 year and is billed at the beginning of each semester for the courses registered during that semester. A non-refundable tuition deposit of $995, which is applied to tuition due the first semester, is required when the applicant accepts the offer of admission.

Admissions Process

• Bachelor’s and master’s degrees in engineering, applied science, business, computer science, or a related field from accredited institutions.
• A minimum graduate-level GPA of 3.2
• Capacity for original scholarship.
• TOEFL, IELTS, Duolingo, or PTE scores are required of all applicants who are not citizens of countries where English is the official language.  Check our  International Students Page  to learn about the SEAS English language requirements and exemption policy. Test scores may not be more than two years old.

Note: GRE and GMAT scores are not required

Please note that our doctoral programs are highly selective; meeting minimum admissions requirements does not guarantee admission.  

• Attach up-to-date Resume 
• Attach Statement of Purpose – In an essay of 250 words or less, state your purpose in undertaking graduate study at The George Washington University. Describe your academic objectives, research interests, and career plans. Discuss your qualifications including collegiate, professional, and community activities, and any other substantial accomplishments not mentioned.
• Online Engineering Programs The George Washington University 170 Newport Center Drive Suite 260 Newport Beach, CA 92660

Normally, all transcripts must be received before an admission decision is rendered for the Doctor of Engineering program. 

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College of Computing

Ph.d. in machine learning, about the curriculum.

The central goal of the Ph.D. program is to train students to perform original, independent research. The most important part of the curriculum is the successful defense of a Ph.D. dissertation, which demonstrates this research ability.

The curriculum is designed with the following principal educational goals:

•    Students will develop a solid understanding of fundamental principles across a range of core areas in the machine learning discipline. •    Students will develop a deep understanding and set of skills and expertise in a specific theoretical aspect or application area of the machine learning discipline. •    The students will be able to apply and integrate the knowledge and skills they have developed and demonstrate their expertise and proficiency in an application area of practical importance. •    Students will be able to engage in multidisciplinary activities by being able to communicate complex ideas in their area of expertise to individuals in other fields, be able to understand complex ideas and concepts from other disciplines, and be able to incorporate these concepts into their own work. The curriculum for the Ph.D. in Machine Learning is truly multidisciplinary, containing courses taught in eight schools across three colleges at Georgia Tech:  •    Computer Science (Computing) •    Computational Science and Engineering (Computing) •    Interactive Computing (Computing) – see Computer Science •     Aerospace Engineering (Engineering) •     Biomedical Engineering (Engineering) •     Electrical and Computer Engineering (Engineering) •     Industrial Systems Engineering (Engineering) •     Mathematics (Sciences) Students must complete four core courses, five electives, a qualifying exam, and a doctoral dissertation defense. All doctorate students are advised by ML Ph.D. Program Faculty . All coursework must be completed before the Ph.D. proposal. An overall GPA of 3.0 is required for the Ph.D. coursework.

Research Opportunities

Our faculty comes from all six colleges across Georgia Tech’s campus, creating many interdisciplinary research opportunities for our students. Our labs focus on research areas such as artificial intelligence, data science, computer vision, natural language processing, optimization, machine learning theory, forecasting, robotics, computational biology, fintech, and more.

External applications are only accepted for the Fall semester each year. The application deadline varies by home school. 

The Machine Learning Ph.D. admissions process works bottom-up through the home schools. Admissions decisions are made by the home school, and then submitted to the Machine Learning Faculty Advisory Committee (FAC) for final approval. Support for incoming students (including guarantees of teaching assistantships and/or fellowships) is determined by the home schools. 

After the admissions have been approved by the FAC, the home school will communicate the acceptance to the prospective student. The home school will also communicate all rejections.

Get to Know Current ML@GT Students

Learn more about our current students, their interests inside and outside of the lab, favorite study spots, and more.

Career Outlook

The machine learning doctorate degree prepares students for a variety of positions in industry, government, and academia. These positions include research, development, product managers, and entrepreneurs. 

Graduates are well prepared for position in industry in areas such as internet companies, robotic and manufacturing companies and financial engineering, to mention a few. Positions in government and with government contractors in software and systems are also possible career paths for program graduates. Graduates are also well-suited for positions in academia involving research and education in departments concerned with the development and application of data-driven models in engineering, the sciences, and computing. 

Frequently Asked Questions

For additional questions regarding the ML Ph.D. program, please take a look at our frequently asked questions.

You can also view the ML Handbook which has detailed information on the program and requirements.

From the Catalog:

• Graduate Studies

Machine Learning and Big Data PhD Track

Optional PhD Tracks:   Statistical Genetics ,  Statistics in the Social Sciences ,  Machine Learning and Big Data

About The UW Department of Statistics now offers a PhD track in the area of Machine Learning and Big Data. All incoming and current students are eligible to apply. The goal of the PhD track is to prepare students to tackle large data analysis tasks with the most advanced tools in existence today, while building a strong methodological foundation. Students in this track will have a multidisciplinary experience, taking courses across departments and interacting with faculty and graduate students from these departments. A similar PhD track is being offered in  Computer Science and Engineering  (CSE), and students from both of these tracks will interact significantly in the core courses.

More details about ML @ UW can be found  here  and  here .

For application details, click  here .

Program Requirements

• Statistics Core:  STAT 570 ,  STAT 581 ,  STAT 582
• ML/BD Core:
• (i) Foundational ML:  STAT 535 (ii) One advanced ML course:  STAT 538  or  STAT 548 (iii) One CSE course:  CSE 544  (Databases) or CSE 512 (Visualization) (iv) One MLBD related elective such as a course from the list below and Two electives from the general electives list:        * Advanced Statistical Learning ( STAT 538 )       * Machine Learning for Big Data ( STAT 548 )       * Graphical Models ( CSE 515 )       * Visualization (CSE 512)       * Databases ( CSE 544 )       * Convex Optimization ( EE 578 )
• All other statistics PhD requirements hold, except  STAT 571  may be used to satisfy the Applied Data Analysis Project.
• STAT 583 is not required.

Advanced Data Science Transcriptable Option A student in the MLBD track can, in addition, choose to enroll/satisfy the Advanced Data Science Option. To further expand students' education and create a campus-wide community, students will register for at least 4 quarters in the weekly  eScience Community Seminar . Satisfying this option means that the student will have "ADS" listed on their transcript.

• eScience ADSO

ML Lunch Series A lunchtime seminar on a topic related to machine learning is held nearly weekly on Tuesdays during term. Lunch is provided. Updates are posted  here .

ML Mailing List General announcements related to machine learning are made on the  ML mailing list .

Machine Learning Research Topics for MS PhD

Machine learning research topic ideas for ms, or ph.d. degree.

I am sharing with you some of the research topics regarding Machine Learning that you can choose for your research proposal for the thesis work of MS, or Ph.D. Degree.

• Applications of machine learning to machine fault diagnosis: A review and roadmap
• Significant applications of machine learning for COVID-19 pandemic
• Quantum chemistry in the age of machine learning
• A survey on machine learning for data fusion
• Artificial intelligence and machine learning to fight COVID-19
• Machine learning for molecular simulation
• A survey on distributed machine learning
• Explainable machine learning for scientific insights and discoveries
• When Machine Learning Meets Privacy: A Survey and Outlook
• Machine learning testing: Survey, landscapes and horizons
• Machine learning and psychological research: The unexplored effect of measurement
• Universal differential equations for scientific machine learning
• Machine learning for active matter
• Exploring chemical compound space with quantum-based machine learning
• Ten challenges in advancing machine learning technologies toward 6G
• Machine learning for materials scientists: An introductory guide toward best practices
• Lessons from archives: Strategies for collecting sociocultural data in machine learning
• Tslearn, a machine learning toolkit for time series data
• A snapshot of the frontiers of fairness in machine learning
• How machine learning will transform biomedicine
• An introduction to machine learning
• Machine learning for protein folding and dynamics
• DScribe: Library of descriptors for machine learning in materials science
• Advances of four machine learning methods for spatial data handling: A review
• New machine learning method for image-based diagnosis of COVID-19
• Applications of machine learning methods for engineering risk assessment–A review
• A critical review of machine learning of energy materials
• State-of-the-art on research and applications of machine learning in the building life cycle
• Elastic machine learning algorithms in amazon sagemaker
• Applications of machine learning to diagnosis and treatment of neurodegenerative diseases
• Assessment of supervised machine learning methods for fluid flows
• Different scaling of linear models and deep learning in UKBiobank brain images versus machine-learning datasets
• First-order and Stochastic Optimization Methods for Machine Learning
• Explainable machine learning in deployment
• Machine learning for enterprises: Applications, algorithm selection, and challenges
• Multiscale modeling meets machine learning: What can we learn?
• Machine learning from a continuous viewpoint, I
• Machine learning applications in systems metabolic engineering
• Single trajectory characterization via machine learning
• Adversarial machine learning-industry perspectives
• Machine learning approaches for thermoelectric materials research
• Machine learning approaches for analyzing and enhancing molecular dynamics simulations
• Open graph benchmark: Datasets for machine learning on graphs
• Preparing medical imaging data for machine learning
• On hyperparameter optimization of machine learning algorithms: Theory and practice
• Machine learning techniques for the diagnosis of Alzheimer’s disease: A review
• CRISPR-based surveillance for COVID-19 using genomically-comprehensive machine learning design
• Combining SchNet and SHARC: The SchNarc machine learning approach for excited-state dynamics
• Personality research and assessment in the era of machine learning
• Machine learning force fields
• Automated machine learning: Review of the state-of-the-art and opportunities for healthcare
• Applications of artificial intelligence and machine learning in smart cities
• Machine learning and wearable devices of the future
• Integrating physics-based modeling with machine learning: A survey
• The non-iid data quagmire of decentralized machine learning
• Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance
• Machine learning and soil sciences: A review aided by machine learning tools
• Machine learning and deep learning techniques for cybersecurity: a review
• Identifying ethical considerations for machine learning healthcare applications
• Introduction to machine learning
• Machine learning for quantum matter
• Machine learning for glass science and engineering: A review
• Machine learning for continuous innovation in battery technologies
• Applying machine learning in science assessment: a systematic review
• Machine learning for interatomic potential models
• Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study
• FCHL revisited: Faster and more accurate quantum machine learning
• Machine-learning-assisted synthesis of polar racemates
• Clinical text data in machine learning: Systematic review
• Machine learning for genetic prediction of psychiatric disorders: a systematic review
• Wake modeling of wind turbines using machine learning
• A survey of surveys on the use of visualization for interpreting machine learning models
• Big-data science in porous materials: materials genomics and machine learning
• Machine learning
• The rise of machine learning for detection and classification of malware: Research developments, trends and challenges
• Building thermal load prediction through shallow machine learning and deep learning
• Machine learning technology in biodiesel research: A review
• Machine learning driven smart electric power systems: Current trends and new perspectives
• What role does hydrological science play in the age of machine learning?
• Early diagnosis of hepatocellular carcinoma using machine learning method
• Image-based cardiac diagnosis with machine learning: a review
• Unsupervised machine learning and band topology
• Cybersecurity data science: an overview from machine learning perspective
• A survey of visual analytics techniques for machine learning
• Quantum embeddings for machine learning
• M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines
• Coronavirus Disease (COVID-19): A Machine learning bibliometric analysis
• Special issue on machine learning and data-driven methods in fluid dynamics
• A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic
• Metallurgy, mechanistic models and machine learning in metal printing
• A perspective on using machine learning in 3D bioprinting
• COVID-19 pandemic prediction for Hungary; a hybrid machine learning approach
• The relationship between trust in AI and trustworthy machine learning technologies
• Improving reproducibility in machine learning research (a report from the neurips 2019 reproducibility program)
• COVID-19 future forecasting using supervised machine learning models
• Mapping landslides on EO data: Performance of deep learning models vs. traditional machine learning models
• A biochemically-interpretable machine learning classifier for microbial GWAS
• Identifying scenarios of benefit or harm from kidney transplantation during the COVID‐19 pandemic: a stochastic simulation and machine learning study
• Machine learning analysis of whole mouse brain vasculature
• Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence
• Machine Learning Calabi–Yau Metrics
• Opening the black box: Interpretable machine learning for geneticists
• Machine learning in additive manufacturing: State-of-the-art and perspectives
• Machine learning approach to identify stroke within 4.5 hours
• Machine-learning quantum states in the NISQ era
• Machine learning as an early warning system to predict financial crisis
• Interpretable machine learning
• Landslide identification using machine learning
• Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning
• Recent advances on constraint-based models by integrating machine learning
• Machine Learning in oncology: A clinical appraisal
• Polymer design using genetic algorithm and machine learning
• Performance evaluation of machine learning methods for forest fire modeling and prediction
• Machine learning approach for confirmation of covid-19 cases: Positive, negative, death and release
• Learning earth system models from observations: machine learning or data assimilation?
• Machine Learning Meets Quantum Physics
• Clinical applications of continual learning machine learning
• Machine learning: accelerating materials development for energy storage and conversion
• Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review
• A review on machine learning forecasting growth trends and their real-time applications in different energy systems
• A systematic literature review on machine learning applications for sustainable agriculture supply chain performance
• Machine learning in geo-and environmental sciences: From small to large scale
• Blockchain and machine learning for communications and networking systems
• Machine learning and natural language processing in psychotherapy research: Alliance as example use case.
• Machine Learning for Solar Array Monitoring, Optimization, and Control
• Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment
• Machine learning in agricultural and applied economics
• AutoML-zero: evolving machine learning algorithms from scratch
• A comprehensive survey of loss functions in machine learning
• COVID-19 epidemic analysis using machine learning and deep learning algorithms
• Attention in psychology, neuroscience, and machine learning
• Get rich or die trying… finding revenue model fit using machine learning and multiple cases
• How machine-learning recommendations influence clinician treatment selections: the example of the antidepressant selection
• Machine learning based solutions for security of Internet of Things (IoT): A survey
• Introduction to machine learning, neural networks, and deep learning
• Machine learning-based design support system for the prediction of heterogeneous machine parameters in industry 4.0
• Machine learning meets mechanistic modelling for accurate prediction of experimental activation energies
• Determinants of base editing outcomes from target library analysis and machine learning
• A primer for understanding radiology articles about machine learning and deep learning
• A machine‐learning approach for earthquake magnitude estimation
• Applying machine learning in liver disease and transplantation: a comprehensive review
• Machine learning approaches for elucidating the biological effects of natural products
• Systematic review of machine learning for diagnosis and prognosis in dermatology
• Early prediction of circulatory failure in the intensive care unit using machine learning
• Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions
• Machine learning applications for mass spectrometry-based metabolomics
• Improving the accuracy of medical diagnosis with causal machine learning
• A machine learning forecasting model for COVID-19 pandemic in India
• Machine learning in psychometrics and psychological research
• Automatic detection of coronavirus disease (covid-19) in x-ray and ct images: A machine learning-based approach
• Machine learning predicts new anti-CRISPR proteins
• Machine learning approaches to drug response prediction: challenges and recent progress
• Machine learning prediction of mechanical properties of concrete: Critical review
• An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications
• Crop yield prediction using machine learning: A systematic literature review
• Julia language in machine learning: Algorithms, applications, and open issues
• The impact of machine learning on patient care: A systematic review
• A machine-learning approach to predicting and understanding the properties of amorphous metallic alloys
• Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge
• Applications of machine learning predictive models in the chronic disease diagnosis
• Your evidence? Machine learning algorithms for medical diagnosis and prediction
• Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review
• Towards the systematic reporting of the energy and carbon footprints of machine learning
• Machine learning accurate exchange and correlation functionals of the electronic density
• Machine learning in additive manufacturing: A review
• Association between weather data and COVID-19 pandemic predicting mortality rate: Machine learning approaches
• Explaining machine learning classifiers through diverse counterfactual explanations
• A deep feature learning model for pneumonia detection applying a combination of mRMR feature selection and machine learning models
• A review of epileptic seizure detection using machine learning classifiers
• Ai explainability 360: An extensible toolkit for understanding data and machine learning models
• Using machine learning to predict decisions of the European Court of Human Rights
• Intelligent edge computing based on machine learning for smart city
• Machine learning and its applications in plant molecular studies
• Machine learning for fluid mechanics
• A universal machine learning algorithm for large-scale screening of materials
• Coronavirus (covid-19) classification using ct images by machine learning methods
• Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential
• A survey of online data-driven proactive 5g network optimisation using machine learning
• Machine learning algorithms for construction projects delay risk prediction
• Toward interpretable machine learning: Transparent deep neural networks and beyond
• Influence of coronary calcium on diagnostic performance of machine learning CT-FFR: results from MACHINE registry
• PyFitit: The software for quantitative analysis of XANES spectra using machine-learning algorithms
• Machine learning-based classification of vector vortex beams
• Machine‐learning scoring functions for structure‐based drug lead optimization
• Potential neutralizing antibodies discovered for novel corona virus using machine learning
• Machine learning and artificial intelligence in haematology
• Machine learning on graphs: A model and comprehensive taxonomy
• Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning
• MadMiner: Machine learning-based inference for particle physics
• Machine learning for asset managers
• Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics
• Statistical and machine-learning methods for clearance time prediction of road incidents: A methodology review
• Hierarchical machine learning of potential energy surfaces
• Hybrid decision tree-based machine learning models for short-term water quality prediction
• Machine-learning studies on spin models
• Machine learning and data analytics for the IoT
• Quantum adversarial machine learning
• Engaging proactive control: Influences of diverse language experiences using insights from machine learning.
• Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping
• Corporate default forecasting with machine learning
• Identification of light sources using machine learning
• Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
• Performance evaluation of different machine learning methods and deep-learning based convolutional neural network for health decision making
• On-demand monitoring of construction projects through a game-like hybrid application of BIM and machine learning
• Recent developments in machine learning for energy systems reliability management
• Machine learning and AI in marketing–Connecting computing power to human insights
• Prediction of criticality in patients with severe Covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in …
• Machine-learning-accelerated perovskite crystallization
• A review on machine learning in 3D printing: Applications, potential, and challenges
• Integrated machine learning methods with resampling algorithms for flood susceptibility prediction
• Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma
• An open source machine learning framework for efficient and transparent systematic reviews
• Machine learning in breast MRI
• Chemist versus Machine: Traditional Knowledge versus Machine Learning Techniques
• Machine learning models for secure data analytics: A taxonomy and threat model
• Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities
• Prediction of droughts over Pakistan using machine learning algorithms
• From real‐world patient data to individualized treatment effects using machine learning: current and future methods to address underlying challenges
• Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI
• Physics-informed machine learning: case studies for weather and climate modelling
• How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?
• Selecting appropriate machine learning methods for digital soil mapping
• Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
• A clinician’s guide to artificial intelligence: how to critically appraise machine learning studies
• Predicting energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability
• Machine learning and artificial intelligence
• COVID-19 diagnosis prediction in emergency care patients: a machine learning approach
• The use of machine learning techniques in trauma-related disorders: a systematic review
• A review of machine learning applications in wildfire science and management
• Land-use land-cover classification by machine learning classifiers for satellite observations—A review
• Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping
• Machine learning for suicidology: A practical review of exploratory and hypothesis-driven approaches
• Fairness in machine learning
• A machine learning-based model for survival prediction in patients with severe COVID-19 infection
• Bridging learning sciences, machine learning and affective computing for understanding cognition and affect in collaborative learning
• Modelling of shallow landslides with machine learning algorithms
• Predicting Regioselectivity in Radical C− H Functionalization of Heterocycles through Machine Learning
• Understanding from machine learning models
• Nothing to disconnect from? Being singular plural in an age of machine learning
• Supervised classification algorithms in machine learning: A survey and review
• Can machine learning find extraordinary materials?
• Incorporating biological structure into machine learning models in biomedicine
• Bitcoin price prediction using machine learning: An approach to sample dimension engineering
• Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0
• Secure, privacy-preserving and federated machine learning in medical imaging
• Assessing and mapping multi-hazard risk susceptibility using a machine learning technique
• Classifying earthquake damage to buildings using machine learning
• The state of the art in enhancing trust in machine learning models with the use of visualizations
• The digital divide in light of sustainable development: An approach through advanced machine learning techniques
• Machine learning-based prediction of COVID-19 diagnosis based on symptoms
• Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine
• Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry
• Predicting standardized streamflow index for hydrological drought using machine learning models
• Machine learning line bundle cohomology
• Machine learning for resource management in cellular and IoT networks: Potentials, current solutions, and open challenges
• Hybrid meta-heuristic and machine learning algorithms for tunneling-induced settlement prediction: A comparative study
• Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous …
• Interpreting Interpretability: Understanding Data Scientists’ Use of Interpretability Tools for Machine Learning
• Secure and robust machine learning for healthcare: A survey
• A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions
• Dynamic backdoor attacks against machine learning models
• Flood susceptibility modelling using advanced ensemble machine learning models
• Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare
• Data-Driven Security Assessment of Power Grids Based on Machine Learning Approach
• Machine learning in rheumatology approaches the clinic
• A novel randomized machine learning approach: Reservoir computing extreme learning machine
• 5G vehicular network resource management for improving radio access through machine learning
• Technologies toward next generation human machine interfaces: From machine learning enhanced tactile sensing to neuromorphic sensory systems
• Transformer fault diagnosis method using IoT based monitoring system and ensemble machine learning
• Machine learning based early warning system enables accurate mortality risk prediction for COVID-19
• Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm
• Data-driven symbol detection via model-based machine learning
• Machine learning models for drug–target interactions: current knowledge and future directions
• Machine‐learning scoring functions for structure‐based virtual screening
• Machine learning and artificial intelligence: Definitions, applications, and future directions
• Role of biological data mining and machine learning techniques in detecting and diagnosing the novel coronavirus (COVID-19): a systematic review
• Closed-loop optimization of fast-charging protocols for batteries with machine learning
• Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy
• Improving risk prediction in heart failure using machine learning
• A perspective on machine learning methods in turbulence modeling
• Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies
• Machine learning in fetal cardiology: What to expect
• Molecular machine learning: the future of synthetic chemistry?
• Application and comparison of several machine learning algorithms and their integration models in regression problems
• A machine learning application for raising wash awareness in the times of covid-19 pandemic
• Modeling and scale-bridging using machine learning: nanoconfinement effects in porous media
• Thirty years of machine learning: The road to Pareto-optimal wireless networks
• A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia
• Predicting and explaining corruption across countries: A machine learning approach
• Synergy Between Expert and Machine‐Learning Approaches Allows for Improved Retrosynthetic Planning
• Machine learning: hands-on for developers and technical professionals
• Conservation machine learning: a case study of random forests
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Reflections on my (Machine Learning) PhD Journey

December 31, 2020

2020 has been an incredibly challenging year, and on a personal note, has marked an important milestone — graduating with my PhD in computer science from Cornell University. This has been a six year journey, where my personal growth as a machine learning researcher (from thrills of first discoveries to the laborious grind through publication rejections to identifying a broader research vision) also took place against the backdrop of the rapid growth and change of the entire field ( 2014 NeurIPS : ~2k attendees, 2020 NeurIPS : ~20k attendees).

With this year coming to an end, I’ve put together some of my reflections and lessons learned from my (Machine Learning) PhD experiences. I discuss topics including expectations going in, common challenges during the PhD (and some strategies for helping with them), keeping up with papers, the community nature of research and developing a research vision. I hope that these topics are helpful in navigating the PhD and research in Machine Learning!

Expectations going into the PhD

Feeling completely stuck, feeling overwhelmed with keeping up with ml progress, feeling isolated, three useful personal skills, keeping notes on papers and ideas, the importance of community, developing a research vision.

In the post title, I’ve referred to the PhD as a “journey”, an aspect often underappreciated, particularly if one is coming straight out of undergrad (which was my experience). A typical Machine Learning PhD is going to be ~5-6 years of relatively unstructured time, and during this, not only will you learn research skills and knowledge about the field, but you’ll also develop personal preferences on how interesting specific problems are, the aesthetics of different subfields, and even perspectives on the type of work being done across academia/industry/policy/nonprofits.

These evolving personal preferences will influence the type of research you decide to work on, and even the post-PhD career path you pick. But particularly at the start of the PhD, it’s hard to predict how these personal perspectives will evolve. In my case, I started my PhD fully assuming I’d stay in industry, part way through began seriously considering academia, and at the end made the very difficult decision to turn down academic offers and stay on in industry. So going into the PhD program, it’s often helpful to take it step by step, and focus on getting the most out of the experience (learning/research/community participation), instead of a very specific desired outcome (which is prone to change, and may also add unnecessary pressure.)

Common Challenges through the Journey

Doing a PhD can be an immensely rewarding experience, and, particularly in Machine Learning, offers the chance to contribute to fundamental scientific understanding as well as impactful technology deployment. I’ve been grateful to my PhD for providing many opportunities to experience both of these! However, the duration and unstructured nature of the PhD can also make it challenging. My journey definitely consisted of ups and downs, and at various times I’ve struggled with feeling isolated, completely stuck, and even overwhelmed by trying to keep up with the rapid pace of progress. Looking back, and through discussion with peers, I now know these low points can unfortunately be quite common. But because these experiences are shared across many people, there can also be strategies for working through them. Below I discuss some of these experiences and strategies.

One very common challenge is feeling completely stuck, either on a specific project or with regards to the research process on the whole.

If the challenge is a specific project, where you’ve pushed hard and it’s still not quite working, then some strategies that might help are

• Making a write up : Collect any partial experimental results, mathematical insights, jotted notes on motivation, etc and take time to put together a write up. This can help with providing a picture of where things stand and where the important gaps are.
• Pivot : if there’s a specific part of the project that’s not working, is there the possibility to reframe the question (possibly taking inspiration from related work) to make it more tractable?
• Forming connections : are there links between what the current project focuses on and other areas of study? Can that connection be explored in this project? This can both help progress and in making the project relevant to a broader community.
• Feedback on writeup : It might also be helpful to get feedback on the project write up from peers, collaborators and friends in the research community. They may be able to offer new perspectives or suggest improvements.
• Workshop submission : it can also be useful to make a workshop submission. This also provides a chance to help collect together all the research results and get useful feedback. (For some time now, I’ve gained the most out of the workshops at machine learning conferences, due to being able to discuss/get feedback on ongoing directions and meeting other researchers working on the same area.)
• Wrap up and move on : Occasionally, there may be a project which sounded promising in the beginning, but has been difficult to make work, and is also inherently challenging to reframe or form connections to other areas. In this (difficult) situation, it may make most sense to wrap up the project quickly and move on. If you have partial results, it’s likely worthwhile to create a final writeup of those and share, so one option is to do this, get confirmation from collaborators and final feedback, and keep it as an arXiv preprint or workshop paper.

If the feeling of being stuck originates from the research process more broadly, one important point I’ve realised is that gaining research maturity can often be very hard to measure, especially when evaluating yourself! Midway through my PhD, I started working on some healthcare applications, and was only making slow headway on learning about the area/writing papers. This had me feeling stuck and somewhat frustrated at my slowdown in research progress. But when I re-read some papers that I’d first come across at the start of my PhD, the depth and context with which I could understand their results was strikingly different from earlier on.

Critical aspects of research maturity — understanding the broader context of results, being able to form connections between different areas, quickly narrowing in on novel key contributions in your subfield — don’t immediately translate to tangible outputs (more papers). But they’re central for becoming an independent researcher with a rich research vision — arguably the main research goal of the PhD. And if you’re reading papers, learning about the field, and working on research directions yourself, (and maybe even teaching/mentoring) most likely you’re making progress on all of these important aspects!

Machine Learning is a vibrant, fast-paced field. But the flipside of this is drowning in the flood of new papers, new preprints, new blogposts, new implementations, new frameworks, etc, etc. (Fun statistic: NeurIPS this past year had ~10k submissions and ~2k accepted papers — no wonder we’re feeling overwhelmed!)

My strategy in dealing with this has been

• Have a number of go-to links to find references to related papers. For me, this has been a combination of (i) subscribing to the arxiv stat.ML cs.LG mailing lists, arXiv-sanity , Twitter, (occasionally) reddit/MachineLearning , paperswithcode and perusing Semantic Scholar / Google Scholar .
• Keep a reading list of papers If I come across an interesting paper but don’t have time to read it then (often the case), I make a note of it and try to return to it later.
• Have a paper reading strategy If a paper is very close to research directions I’m actively working on, I’ll read it in detail, otherwise I’ll skim the abstract to get a high level picture.
• Occasionally read up on different areas Occasionally (maybe once per year), I’ll look into a few interesting areas I’m not working on, and read several papers in each to get a sense of what is being worked on.

It’s also helpful to remember that (i) everyone feels overwhelmed with the rate of publishing and (ii) many papers may rely on the same underlying idea, and often being familiar with the idea is enough for keeping up with the field.

Another common challenge in the PhD is struggling with feelings of isolation. In the first couple of years of my PhD, some projects required that I kept laser focus on very narrow, specific questions, which were also highly laborious and (felt) never-ending. During those times, it was hard not to feel completely cut-off from other researchers and the broader field, and I’m very grateful for all the support and guidance from my PhD advisor in pushing through that situation.

More broadly, this scenario can be common especially earlier on during the PhD, where you might simultaneously be learning how to see through a research project from start to finish, and at the same time have less context and connections to the broader research field/community. Maintaining connections to the field/community can be very helpful in making sure you don’t feel isolated. Some ways to do this could be: (i) setting up collaborations with (senior) students/postdocs (ii) getting feedback on your work in progress – this might be your advisor/lab, but could also be other peers/mentors working in the field (iii) actively participating in the broader community, whether that’s through simply attending conferences, mentoring or organizing workshops.

Having discussed some of the common challenges faced in the PhD and ways to help address them, the rest of this post will overview some useful considerations for research progress.

In particular, I’ll begin by discussing three personal skills I found to be very helpful throughout the PhD: initiative, focus and perseverance. This is of course drawing on my personal experiences, and there are varying opinions on useful personal skills! But for me, coming out of undergrad, a key difference I noticed in the PhD program was the need to take initiative — whether that meant reading important relevant papers, doing rapid preliminary studies of the feasibility of different approaches, talking to peers doing related research, or even attending and being an active participant in conferences. Because the PhD broadly consists of unstructured time, being productive largely relies on your initiative for learning and conducting research.

Two other skills that I’ve found very helpful are focus and perseverance. When getting started with a new research direction, focus is very helpful to peruse related work, distill the key points, quickly learn from initial exploration and determine the main project directions. Perseverance on the other hand is very useful (especially) for wrapping up the research project: there’s often a long tail of edits/additions for the paper to be submission ready, and post-submission, further edits to respond to peer-review and paper rejections. It can be hard to muster the motivation to make all of these edits (especially when preparing the paper for yet another resubmission, and having newer, more interesting projects also going on), but the variability of the peer-review process often means it’s worth persevering through.

Through my PhD, there are two documents, one started in my first year, and the other in my third year, that I’ve continuously kept updated. The first keeps track of papers that I’ve read – every time I read a new paper, I add it into the doc, along with a short summary of my takeaways. The document is now over fifty pages (which maybe means I should switch to Mendeley or Paperpile), and has been a very useful way to quickly flick back to papers I’ve read years earlier and get key points. The other document keeps track of research ideas. Everytime I have a promising new idea, I make a note of it. Over time, this has helped inform my research directions and highlight key themes.

One important property of (Machine Learning) research that took me time to realise is that research is fundamentally a community endeavor. The problems that we aim to tackle are incredibly difficult, and progress relies on the cycle of you building off of others’ ideas and others building off of your ideas. This is a crucial factor to keep in mind when exploring research directions. What is the community excited about, and why? Are there shortcomings or gaps? Are there natural next steps to study? Taking the time to discuss these questions and others with peers in the community is vital to developing well-informed, germane research questions. And if you identify an exciting, new research direction of interest to the field, it’s often useful to build a community around that direction — this can happen through initiating collaborations, disseminating key open questions and organizing workshops. From very early on in my PhD, I was interested in understanding the key empirical phenomena exhibited by our modern deep learning systems. But working on this topic then was very challenging. The field was evolving rapidly, making the focus of any kind of analysis a moving target, and significantly adding to the challenge of building a new community around this topic. So publishing my first deep learning analysis papers was pretty difficult, and definitely an act of perseverance! But since then, it’s been wonderful to witness and contribute to the growth of this exciting research area!

While I described earlier that when getting started with the PhD, it may be better to take things one step at a time and focus on the experience instead of a specific goal, from the research maturity perspective, the PhD does have a specific goal: to make you an independent researcher, with a rich (articulable) research vision.

In current Machine Learning research, with the deluge of papers, it’s easy to feel stressed about the need to continuously churn out publications. But while paper writing is an important skill, I think the crucial test of research maturity is being able to have knowledgeable perspectives on your field which help identify key research questions, connected by overarching themes — a research vision.

Having a well developed research vision is enormously motivating. As an analogy, it’s a little like completing a “paint by numbers” kit: instead of just seeing the color of each individual square, you suddenly appreciate the full picture.

So how does one develop a research vision?

As a first note, from my PhD journey, I think it’s hard to develop a full-fledged research vision without some years of research experience. In my first couple of years of PhD, I remember reading papers and watching talks of senior researchers, and being frustrated that I couldn’t identify/articulate interesting research questions nearly as well. In the years since then, the compounding effects of all the papers I’ve read, projects worked on, seminars attended, have significantly improved my ability to do this. (There is of course room to improve! Going forwards, this ability will continue to develop, as I gain more context and understanding of larger subfields.)

Being more specific about the stages that led to a (better developed) research vision: it started off with exploration, with my first few projects giving me diverse exposures and helping me understand what I found intrinsically interesting. From there, there were natural followup projects to study, which finally also led to some related questions on applications/deployment. All of this started coming together under the broad theme of Machine Learning Design and Human-AI interaction at Deployment, and, as research visions are good at doing, also inspired new questions. (I am deeply grateful to my PhD advisor for his insights, guidance and encouragement through all of this!)

As a final point, I want to emphasise that the years of experience really do have a compounding effect. As you work on research projects, it becomes easier to identify the salient ideas in research papers, this informs your personal perspectives and promising questions for next projects, working on these next projects makes it easier to absorb/give talks, which then circles back to help with identifying new interesting research directions, which eventually coalesce to form a broader vision.

In summary, doing a PhD can be very fulfilling. It is however a journey, and has its ups and downs, personal discoveries, and evolution of (research) perspectives. I’m very grateful for the many rich experiences during my PhD, and hope this post might be helpful for others on the journey!

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Latest phd thesis topics in machine learning.

• With progressive technological development, the exploration of machine learning has increased a huge number of applications. Consequentially. Machine learning instigates an essential part of implementing smart and automated applications by intelligent data analysis.
• The applicability of machine learning is abundant in many real-world application fields, such as predictive analytics and intelligent decision-making, cyber-security systems, smart cities, healthcare, e-commerce, agriculture, finance, retail, social media, traffic prediction and transportation, computer vision applications, user behavior analytics and context-aware smartphone applications, bioinformatics, cheminformatics, computer networks, DNA sequence classification, economics and banking, robotics, advanced engineering, and many more.
• Recently described branches of machine learning are computational learning theory, adversarial machine learning, quantum machine learning, robot learning, and meta-learning. Efficient data processing and handling the diverse learning algorithms are the constraints that are needed to be the focus in machine learning. PHD thesis on machine learning contributes to the challenges, promising research opportunities, and effective solutions in various application areas. Below is the list of PHD thesis topics on machine learning to effectively explore, scrutinize and discover the new findings of machine learning systems.

List of Sample PHD Thesis in Machine Learning

• Incremental Learning for Large-Scale Data Stream Analytics in a Complex Environment
• Neural Sequential Transfer Learning for Relation Extraction
• Predicting Depression and Suicide Ideation in the Canadian Population Using Social Media Data
• Neural Aspect-based Text Generation
• Leveraging Social Media and Machine Learning for enhanced Communication and Understanding between Organizations and Stakeholders
• Deep Learning Methods for Short,Informal, and Multilingual Text Analytics
• Deep Learning Based Cursive Text Detection and Recognition in Natural Scene Images
• Deep Learning-Based Text Detection and Recognition
• Explaining Deep Neural Networks
• Machine Learning Techniques in Spam Filtering
• Anomaly-Based Network Intrusion Detection Using Machine Learning
• Machine Learning for Financial Products Recommendation
• Sentiment Analysis of Textual Content in Social Networks
• Deep Learning For Time Series Classification
• Deep Learning for Traffic Time Series Data Analysis
• Novel applications of Machine Learning to Network Traffic Analysis and Prediction
• Deep Learning for Animal Recognition
• Neural Transfer Learning for Natural Language Processing
• Scalable and Ensemble Learning for Big Data
• Ensembles for Time Series Forecasting
• Sample-Efficient Deep Reinforcement Learning for Continuous Control
• Towards Generalization and Efficiency in Reinforcement Learning
• Transfer Learning with Deep Neural Networks for Computer Vision
• Deep Learning for Recommender Systems
• CHAMELEON: A Deep Learning Meta-Architecture For News Recommender Systems
• Learning in Dynamic Data-Streams with a Scarcity of Labels
• Learning Meaning Representations For Text Generation With Deep Generative Models
• Social Media Sentiment Analysis with a Deep Neural Network: An Enhanced Approach Using User Behavioral Information
• Global-Local Word Embedding for Text Classification
• Measuring Generalization and Overfitting in Machine Learning
• Handling Class Imbalance Using Swarm Intelligence Techniques, Hybrid Data and Algorithmic Level Solutions
• Using Data Science and Predictive Analytics to Understand 4-Year University Student Churn
• Deep Learning Based Imbalanced Data Classification and Information Retrieval for Multimedia Big Data
• Improving Geospatial Data Search Ranking Using Deep Learning and User Behaviour Data
• An Investigation Into Machine Learning Solutions Involving Time Series Across Different Problem Domains
• Deep Learning Applications for Biomedical Data and Natural Language Processing
• Deep Neural Network Models for Image Classification and Regression
• Deep learning for medical report texts
• Deep multi-agent reinforcement learning
• Artificial intelligence methods to support people management in organisations
• An Intelligent Recommender System Based on Short-term Disease Risk Prediction for Patients with Chronic Diseases in a Telehealth Environment
• Bringing Interpretability and Visualization with Artificial Neural Networks
• Investigating machine learning methods in Recommender systems
• Adaptive Machine Learning Algorithms For Data Streams Subject To Concept Drifts
• Active Learning for Data Streams
• Heart Diseases Diagnosis Using Artificial Neural Networks
• Advanced Natural Language Processing and Temporal Mining for Clinical Discovery
• Uncertainty in Deep Learning
• Parallel Transfer Learning: Accelerating Reinforcement Learning in Multi-Agent Systems
• Sentiment analysis on students Real-time Feedback
• Aspect-Based Opinion Mining From Customer Reviews
• Word Embeddings for Natural Language Processing
• On Effectively Creating Ensembles of Classifiers
• Design of Intelligent Ensembled Classifiers Combination Methods
• ELSE: Ensemble Learning System with Evolution for Content Based Image Retrieval
• Using Hybrid Algorithm to Improve Intrusion Detection in Multi Layer Feed Forward Neural Networks
• Achieving Consistent Near-Optimal Pattern Recognition Accuracy Using Particle Swarm Optimization to Pre-Train Artificial Neural Networks
• Using Assessments of Contextual Learning to Identify Characteristics of Adaptive Transfer in Medical Students
• Recursive Deep Learning for Natural Language Processing and Computer Vision
• Machine learning strategies for multi-step-ahead time series forecasting
• General Attention Mechanism for Artificial Intelligence Systems
• Defense Acquisition University: A Study of Employee Perceptions on Web Based Learning Transfer
• Incremental Learning with Large Datasets
• Machine Learning and Data Mining Methods for Recommender Systems and Chemical Informatics
• Transfer of Learning in Leadership Development: Lived Experiences of HPI Practitioners
• Online Ensemble Learning in the Presence of Concept Drift
• Learning From Data Streams With Concept Drift
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PhD Admissions

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The Computer Science Department PhD program is a top-ranked research-oriented program, typically completed in 5-6 years. There are very few course requirements and the emphasis is on preparation for a career in Computer Science research. 

Eligibility

To be eligible for admission in a Stanford graduate program, applicants must meet:

• Applicants from institutions outside of the United States must hold the equivalent of a United States Bachelor's degree from a college or University of recognized good standing. See detailed information by region on  Stanford Graduate Admissions website. 
• Area of undergraduate study . While we do not require a specific undergraduate coursework, it is important that applicants have strong quantitative and analytical skills; a Bachelor's degree in Computer Science is not required.

Any questions about the admissions eligibility should be directed to  [email protected] .

Application Checklist

An completed online application must be submitted by the CS Department application deadline and can be found  here .

Application Deadlines

The online application can be found here . You may submit one application for a PhD program per respective academic term.

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Best Doctorates in Machine Learning: Top PhD Programs, Career Paths, and Salaries

If you want to take your career in machine learning to the next level, you might be considering enrolling in one of the best PhDs in machine learning. However, it can be hard to figure out which program is right for you, how to fulfill all the requirements, or how to secure the right funding opportunities so you can continue your education in this field.

This comprehensive guide will look at the best options for a machine learning PhD, both in-person and online. We’ll also discuss the best machine learning jobs and how to get them with this type of degree, as well as the average PhD in machine learning salary you can earn upon graduation.

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What is a phd in machine learning.

A PhD in machine learning is a research-intensive degree program that helps students further their education in machine learning. A machine learning PhD is a doctorate degree that involves coursework, qualifying exams, and oral examinations. Professors and members of faculty also work closely with students to help them develop a strong dissertation throughout their degree program.

Students interested in pursuing a PhD in machine learning should have already completed a bachelor’s degree in a relevant field. They also need to have completed a master’s degree , or commit to completing it along the way.

How to Get Into a Machine Learning PhD Program: Admission Requirements

The admission requirements to get into a machine learning PhD program typically include filling out an application form and submitting an application fee, academic transcripts from your undergraduate degree, two to three recommendation letters, a statement of purpose, GRE scores, a resume, writing sample, and English proficiency test scores for international students.

Each school’s website will have a detailed breakdown of all the requirements needed for the application process. Some schools may require you to pay an application fee, have a minimum GPA score, and take the Graduate Record Examination (GRE), although most schools have waived this requirement until 2023.

You will need two or three recommendation letters for your PhD application. The recommendation letter should be from faculty members and colleagues familiar with your work. Part of the application process is a statement of purpose, which is an essay that should tell the admission committee why you want to pursue a PhD in Machine Learning.

PhD in Machine learning Admission Requirements

• Application form
• Application fee
• College transcripts
• Minimum GPA of 3.0 (varies)
• Two to three recommendation letters
• Statement of purpose
• Writing sample
• English proficiency test (only for non-native English speakers)

Machine Learning PhD Acceptance Rates: How Hard Is It to Get Into a PhD Program in Machine Learning?

It is hard to get into a PhD program in machine learning. Prestigious schools are usually very selective and have a low admission rate ranging between four and 30 percent. For example, Harvard University has an admission rate of  four percent, so make sure you prepare a strong application and have a high GPA if you want to get into Harvard or another highly-reputable university.

However, not all PhD programs are extremely selective. For instance, institutions in the University of California system have higher acceptance rates, such as 34.4 percent. To improve your chances of acceptance, you can ask a friend or mentor to look over your PhD application. You should also apply to more than one program.

How to Get Into the Best Universities

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Best PhDs in Machine Learning: In Brief

Best Universities for Machine Learning PhDs: Where to Get a PhD in Machine Learning

The best universities for machine learning PhDs include Carnegie Mellon University, Georgia Tech, and University of Washington. These schools can help you earn your machine learning PhD. If you’re wondering where you can get a PhD in machine learning, the list below discusses 10 excellent programs, along with their essential details.

Carnegie Mellon University was founded in 1900. It is known for its high-quality graduate programs in engineering, artificial intelligence (AI), and computer science. There are about 29 graduate degree programs offered at Carnegie Mellon University’s graduate school. Students and faculty conduct open and restricted research in four main areas, including AI, learning sciences, robotics, and neuroscience.

PhD in Machine Learning

The PhD in Machine Learning at Carnegie Mellon University requires students to take six core courses and one elective course. This research-focused degree program requires students to present and defend a thesis by the end of the program.

During this program, students need to work as teaching assistants for two semesters and will complete a presentation to show adequate presentation skills to the Speaking Skills committee. Common courses for this program include an introduction to machine learning, intermediate statistics, and regression analysis.

PhD in Machine Learning Overview

• Program Length: 5 years
• Acceptance Rate: 17%
• Tuition and Fees: $645/unit
• PhD Funding Opportunities: Graduate assistantships, scholarships, and grants

PhD in Machine Learning Admission Requirements

• GRE (recommended)
• TOEFL (for international applicants)
• Recommendation letters
• High level of knowledge in computer science and math

Georgia Institute of Technology is a reputable university founded in 1885. It is known for its excellent STEM majors, of which 86 percent of students are enrolled. It offers many graduate degree programs to its 25,011 graduate students, but the most well-known programs are in electrical and computer engineering, computer science, and mechanical engineering.

The PhD in Machine Learning at Georgia Institute of Technology will teach you excellent machine learning techniques through machine learning courses. Students need to complete four core courses, five elective courses, responsible conduct of research course, and three doctoral minors.

Typical courses for this PhD program include machine learning theory and methods, advanced theory, and computing and optimization. This program consists of many research hours and requires PhD students to complete the defense of a dissertation. Students also need to complete a qualifying exam.

• Program Length: 4 years
• Acceptance Rate: 21%
• Tuition and Fees: $586/credit (in state); $1,215/unit (out of state)
• PhD Funding Opportunities: Federal loans, private loans, federal work-study program
• Minimum GPA of 3.0
• Three letters of recommendation
• IELTS minimum score of 7.5 or higher for non-native speakers
• TOEFL minimum score of 100 or higher for non-native speakers
• GRE (optional)

Harvard University is a highly reputable and well-known private research university founded in 1636. It currently has about 33,276 students enrolled in undergraduate degrees, graduate degrees, and certificate programs. Harvard University has 12 graduate schools and a fantastic faculty, of which members have received Nobel prizes in chemistry, medicine, physics, literature, peace, and economic sciences.

PhD in Computer Science

The machine learning PhD program at Harvard University teaches students about the interaction of computation with the world and computation fundamentals. Students will work with highly-rated faculty members conducting research in programming languages, machine learning, and artificial intelligence during this excellent program. As they move through their program, students will learn about connecting computer science to other fields while they interact with lawyers, scientists, and engineers.

PhD in Computer Science Overview

• Acceptance Rate: 4%
• Tuition and Fees: $50,928/year
• PhD Funding Opportunities: Grants, fellowships, traineeships, research assistantships, and teaching fellowships

PhD in Computer Science Admission Requirements

• Transcripts
• At least one recommendation letter
• Show English proficiency (for non-native English speakers)

Northwestern University was launched in 1851 and is one of the top research universities in the country. Its more than 50 research centers focus on topics like nanotechnology, neuroscience, biotechnology, and drug discovery.

Currently, Northwestern university has over 13,000 grad students enrolled in its 173 graduate degree and certificate programs. Northwestern University is known for its fantastic business, education, and materials engineering degree programs.

PhD in Computer Science and Learning Sciences

The machine learning PhD program at Northwestern University is research-driven and helps students understand and build a connection between research on computation and learning. Students can choose between many different areas of study, including machine learning and programming language design.

To complete this program, there should be apparent relevance in your research between computer science and the learning science in your field of study, such as machine learning. You must also complete a qualifying exam, research projects, and a PhD dissertation. Courses include Machine Learning, Foundations of Learning Science, and Artificial Intelligence Programming.

PhD in Computer Science and Learning Sciences Overview

• Program Length: 4-9 years
• Acceptance Rate: 7%
• Tuition and Fees: $18,689/quarter for programs with 8 or fewer quarters; $4,672/quarter for more than 8 quarters
• PhD Funding Opportunities: Assistantships, grants, and fellowships

PhD in Computer Science and Learning Sciences Admission Requirements

• Online application form
• Academic transcripts
• GRE scores (temporarily not required, but still recommended)
• TOEFL scores (for international applicants) 

Tulane University was launched in 1834 and is in the top two percent of research universities in the US. Tulane University conducts research in bio-innovation, health, energy, and the environment. It offers over 200 graduate degrees to over 5,000 grad students.

Students at Tulane University graduate school can pursue PhDs in computer science, environmental health studies, economics, and more. This University offers excellent funding opportunities such as fellowships and stipends.  

The PhD in Computer science at Tulane University is a research-intensive program. Students must conduct research in a specific depth area such as machine learning, artificial intelligence, or data science. Students who specialize in machine learning will research machine learning techniques, theory of applications, machine learning systems, and algorithms.

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Topics covered during this graduate program include algorithms, machine learning, and computer networks. Students also need to take three research courses. Students will do a qualifying oral exam during this program, complete a prospectus presentation, and a PhD dissertation in their preferred specialization, such as machine learning.

• Program Length: 4-7 years
• Acceptance Rate: 9.73%
• Tuition and Fees: $1,831/credit; $35,088/year with 9 credits per semester 
• PhD Funding Opportunities: Scholarships, fellowships, and stipends
• University transcripts
• Statement of Purpose
• GRE test scores
• TOEFL scores (for international applicants)

University of California Irvine is a public land-grant university established in the 1960s as part of the University of California system. It is a research-focused institution and boasts eight  Nobel Prize winners among its alumni. The graduate school offers over 100 graduate programs. This university offers many different PhDs, including bioengineering, machine learning and data science, and mechanical engineering.

The PhD in Computer Science at University of California Irvine helps students learn computer science fundamentals and essential machine learning skills. This program involves a research project. Students need to choose a research topic such as machine learning and artificial intelligence, scientific computing, or any other research topic listed on the website. 

Students need to complete at least 47 units during their program and maintain a 3.5 GPA. Courses for this degree include Machine Learning, Machine Learning and Data Mining, and Analysis of Algorithms. Before the end of the program, students will complete a candidacy exam, submit a dissertation plan, complete a final exam, and defend their dissertations. 

• Program Length: 6-7 years
• Acceptance Rate: 28.96%
• Tuition and Fees: $18,037/year (in state); $33,139/year (out of state)
• PhD Funding Opportunities: Fellowships, graduate employment, research assistantships, and training grants
• English proficiency test scores (for international applicants)

University of California San Diego traces its roots back to 1960 and had its first enrollments in 1964. It offers over 200 degree programs at the undergraduate and graduate levels. It is a research-focused institution that conducts research in a variety of fields, from robotics and climate to microbiomes.

PhD in Machine Learning and Data Science

The PhD in Machine Learning and Data Science program teaches students essential machine learning techniques to help them further or start their careers in machine learning and artificial intelligence . During this graduate program, students need to complete 36 credit hours. They will conduct an in-depth research project, a preliminary exam, and a qualifying exam.

At the end of the PhD, students need to submit and defend a doctoral thesis. They are allowed to consider the faculty and choose a research advisor that fits their research style and goals. The research advisor will support the student through their PhD from start to finish. Courses included in this degree are Linear Algebra & Application, Deep Learning & Applications, Machine Learning for Image Processing, and Statistical Learning.

PhD in Machine Learning and Data Science Overview

• Program Length: 6-8 years
• Acceptance Rate: 34.3%
• Tuition and Fees: $ 11,442/year 
• PhD Funding Opportunities: Fellowships

PhD in Machine Learning and Data Science Admission Requirements

• GRE test scores (recommended)
• English proficiency test (for international applicants)
• Three recommendation letters
• High school and college transcripts

University of Pennsylvania is a research-driven university based in Philadelphia. It opened its doors to students in 1751. It prides itself on research and encourages students to conduct research during their studies. This university has twelve graduate schools that offer graduate degrees and certificates. Some of the fields for PhD level studies include biochemistry, economics, and materials science and engineering.

PhD in Computer and Information Science

The PhD in Computer and Information Science at the University of Pennsylvania has a specialization called Machine Learning + X, allowing students to choose machine learning and one other specialization to focus on throughout their programs. For example, you could choose to do a Machine Learning + Computer Architecture specialization.

This degree requires specific courses, a preliminary exam, a teaching assistantship, a defense proposal, a defense of your dissertation, and a submission of your thesis. This PhD will help students gain new machine learning skills and experience in machine learning.

PhD in Computer and Information Science Overview

• Tuition and Fees: $19,919/year for the first eight semesters; $1,836 flat rate after the first eight semesters
• PhD Funding Opportunities: Fellowships, teacher assistantships, and research assistantships

PhD in Computer and Information Science Admission Requirements

• Personal statement
• Unofficial academic transcripts
• Three official recommendation letters
• GRE scores (optional until 2023, but still recommended)

This public research university was established in 1895 and is known for its high-quality doctoral research. University of Texas at Arlington has more than 174 graduate degrees and other graduate study options. New and current students can pursue a PhD in different fields like computer science, civil engineering, and mathematics. 

The PhD in Computer Science offered by University of Texas at Arlington allows students to choose a study track. There are eight options, but students interested in machine learning should choose the intelligent systems track, which covers machine learning methods, neural networks, parallel AI, and more.

Throughout this degree program, students will complete 18 hours of coursework and complete two comprehensive exams, one of which is a dissertation proposal. They will also submit a final dissertation defense before being awarded their PhD.

• Program Length: 4-5 years
• Acceptance Rate: Not stated
• Tuition and Fees: $11,044/year (in state); $23,486/year (out of state)
• PhD Funding Opportunities: Teacher’s assistantships, research assistantships, fellowships, grants, and scholarships
• College transcripts 

University of Washington is a highly reputable school located in Washington that started conducting classes in 1861. It is known for its high-quality research and boasts that seven of its researchers have won Nobel prizes in physics, physiology, and medicine.

New and current students at University of Washington can choose to continue their education with over 300 graduate degree programs offered at its three campuses. This university provides PhDs in physics, mathematics, and machine learning and big data.

PhD in Machine Learning and Big Data

The PhD in Machine Learning and Big Data program at University of Washington teaches students valuable machine learning methods and how to conduct data analysis of big data sets. It will help students build a strong foundation in machine learning and big data methodologies.

Students need to meet the coursework requirements, write a general examination, conduct research to write a dissertation, and meet the credit hour requirement of 90 credits. Courses included in this PhD are Foundational Machine Learning, Advanced Machine Learning, and Advanced Statistical Learning.

PhD in Machine Learning and Big Data Overview

• Program Length: Up to 10 years
• Acceptance Rate: 10.58%
• Tuition and Fees: $6,725/quarter (in state); $11,688/quarter (out of state)
• PhD Funding Opportunities: Fellowships, internships, and research assistantships

PhD in Machine Learning and Big Data Admission Requirements

• GRE scores (optional)
• Funding application

Can You Get a PhD in Machine Learning Online?

No, you cannot get a PhD in machine learning online. However, you can pursue an online PhD in computer science with a machine learning component such as an online machine learning course or specialization. Many fantastic online computer science PhDs will help you fine-tune your machine learning expertise.

Best Online PhD Programs in Machine Learning

How Long Does It Take to Get a PhD in Machine Learning?

It takes between four and 10 years to get a PhD in Machine learning. According to Statista, the average time to complete a doctorate degree is seven and a half years. A PhD takes this long to complete because it is research-intensive and involves several stages.

Students need to take required courses and complete coursework in the first two years of a PhD program. Once the coursework is complete, students will write an examination to ensure they have completed all the essential skills and expertise in machine learning.

In the final years of a PhD, students conduct research and write a dissertation which takes between two to five years to finish. Usually, the school will have information on their website regarding the maximum time students have to meet all the PhD requirements.

Is a PhD in Machine Learning Hard?

Yes, a PhD in Machine Learning is hard because it is research-driven. If you decide to pursue a PhD in machine learning, you need to ensure that you are motivated and determined to work hard because this program involves many hours of independent research and writing.

A PhD is also a lengthy degree program that takes a minimum of four years to complete. Don’t let the difficulty of a PhD in machine learning discourage you, though. If you are determined and enjoy researching and learning about machine learning, you will succeed.

How Much Does It Cost to Get a PhD in Machine Learning?

It costs $19,314 annually to get a PhD in Machine Learning , according to the figures from 2019 stated by the National Center for Education Statistics (NCES). The total tuition of your machine learning PhD depends on specific factors, including format, location, school, and specialization.

Colleges and universities are usually public or private institutions. Depending on what kind of school you attend, the tuition will differ. The average tuition for a PhD at a public institution is $12,171, while a PhD at a private institution costs $25,929. Search your school’s website or contact it directly to learn about the specific tuition costs of your PhD program.

How to Pay for a PhD in Machine Learning: PhD Funding Options

The funding options that students can use to pay for their PhD in machine learning include research assistantships, teaching assistantships, fellowships, internships, grants, and stipends. These funding options will lighten the financial burden of pursuing a PhD in machine learning.

Some schools offer teaching assistantships to students. You work a certain number of hours per week and receive a stipend or a tuition waiver or discount. A research assistantship is similar to a teaching assistantship, but they have different duties. According to Statista, research assistantships are the most common funding option for doctoral degrees .

Find out directly from your school if there are available paid internships, along with any other funding opportunities for PhD students in machine learning. Some schools award funding opportunities to students nominated by faculty members.

Best Online Master’s Degrees

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What Is the Difference Between a Machine Learning Master’s Degree and PhD?

The difference between a machine learning master’s degree and a PhD is that a PhD is research-intensive and focused, while a master’s degree is more focused on one’s career and may or may not include research for a master’s thesis.

A PhD is the highest degree level that a person can pursue, whereas a master’s degree is one level below. According to Statista, PhD degree holders make more than master’s degree graduates . Upon completing a master’s degree, students can earn an average salary of $92,272, while PhD graduates earn an average salary of $136,702.

Master’s vs PhD in Machine Learning Job Outlook

You can get a job as a computer information research scientist with a master’s degree, which comes with a job outlook of 22 percent . This is much faster than the average job outlook. With a PhD in machine learning, you can get any job in machine learning, but a job that explicitly requires a PhD is a university lecturer.

The job outlook for a machine learning lecturer is 12 percent , according to information cited by the US Bureau of Labor Statistics (BLS). This job outlook is much lower than that of a computer information research scientist. However, 12 percent is still an excellent average growth rate.

Difference in Salary for Machine Learning Master’s vs PhD

There is a significant contrast in earnings between a Machine learning PhD and a Machine learning Master’s degree. Although PayScale does not list the salary of Machine learning graduates specifically, it lists salary information for artificial intelligence, a field closely related to machine learning.

The average salary of an artificial intelligence PhD graduate is $115,000, while an AI master’s degree graduate earns an average salary of $103,000, annually . As you can see, a PhD will get you a very high average annual wage, and your salary can increase depending on your experience, location, and position.

Related Machine Learning Degrees

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Why You Should Get a PhD in Machine Learning

You should get a PhD in machine learning because it will open up new job opportunities, help you earn a higher salary, and allow you to add value to the machine learning industry. If you enjoy doing research, learning new things, and want to earn a higher salary, then a PhD is perfect for you.

Reasons for Getting a PhD in Machine Learning

• Higher salaries. Earning a PhD ensures that you will get a job with a high-paying salary. A PhD is the highest degree level that you can achieve, and PhD graduates earn a significantly higher salary than associate, bachelor’s, or master’s degree holders.
• Contributing to your professional industry. While completing a PhD, students conduct a lot of original research, broaden their skills and add value to their field. At the end of a PhD, students submit a dissertation, a document that identifies a problem within the industry and presents a solution through research.
• Enhancing specialized and soft skills. A PhD will help you improve and gain valuable specialized skills and techniques in machine learning, such as statistics and natural language processing. You will also gain excellent soft skills in machine learning, like problem-solving and time management.
• Increasing job opportunities. Once you earn your PhD, your job opportunities will increase. A PhD will help you get a senior profession, such as a lecturer or senior machine learning engineer. According to PayScale, a senior machine learning engineer earns an annual wage of $153,255 .
• Gaining valuable knowledge. Due to a PhD’s research-intensive nature, students constantly learn new things and gain valuable knowledge. If you enjoy learning, you should get a PhD because the learning opportunities are endless.

Getting a PhD in Machine Learning: Machine Learning PhD Coursework

Getting a PhD in Machine Learning requires taking specific courses to meet the necessary credit hours to graduate from your PhD program. Required courses typically include machine learning, introduction to AI, and statistical learning. Machine learning PhD coursework will help you gain essential machine learning skills and knowledge.

During the machine learning course, students will learn about the fundamental topics and techniques in machine learning, such as logistic regression, clustering, classifications, deep neural networks, linear models, and support vector machines. This course encourages reinforcement learning by looking at several real-world examples.

Deep Learning

Deep learning is an essential part of machine learning and involves artificial neural networks. The deep learning course will teach students about theoretical and practical aspects of deep learning, including neural networks, optimization algorithms, and structured models.

Statistical Learning

This course will cover modern learning algorithms such as variational approximations, boosting, and support vector machines. While completing the statistical learning course, students will learn about statistical algorithms for data analysis and applications of signal processing. Students should know programming languages to enroll in this course.

Introduction to Artificial Intelligence

While completing a PhD in machine learning, students will need to complete an Artificial Intelligence course. An Introduction to AI course involves the study of models and theories related to systems that emulate human intelligence. Students will cover Bayesian networks, constraint satisfaction, probabilistic reasoning, and natural language processing.

Analysis of Algorithms

The analysis of algorithms course looks at different efficient algorithms and studies their complexity and correctness. Topics covered include network flow, dynamic programming, and amortized analysis. Students will discuss problems with no solutions and cover all different kinds of algorithms.

Best Master’s Degrees

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How to Get a PhD in Machine Learning: Doctoral Program Requirements

Read the list below to find out how to get a PhD in Machine Learning. There are specific criteria that each student needs to meet before being awarded their degree. Common requirements include the completion of coursework, a research project, and a final thesis.

A machine learning PhD usually requires 40 to 48 credit hours. Students must take about six core courses and one elective course. During the first four semesters of their programs, students need to complete a specific number of credits before the next stage of their PhD.

Research is a considerable part of a PhD, so most programs will require students to take one or more responsible conduct of research courses. The responsible conduct of research courses involves seminars and workshops that help students learn the best methods of conducting research. Some research courses involve a project that will help students learn through practice. 

Machine learning PhDs will include a research project after completing the required research courses. The research project will be directed by a faculty member and requires students to conduct research and write a report. Students will then present their reports to the PhD committee. Research projects usually focus on a specific topic within machine learning or computer science.

Once students have completed the core course requirements and written their research project, they must complete a qualifying exam which typically includes an oral examination. The PhD committee sets the qualifying exam and is designed to assess whether students are ready to conduct independent research for their PhD thesis.

You need to act as a teaching assistant for two semesters in a machine learning course. This is a requirement that only some PhD programs have. The graduate chair and coordinator set the requirements of the teaching practicum.

The PhD thesis requires a few years of research around a specific topic in machine learning. Students research a particular topic, and then they need to present their findings to the PhD committee. The thesis also includes a defense of the dissertation. Usually, students need to submit a thesis draft to the committee for approval before defending it.

Potential Careers With a Machine Learning Degree

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PhD in Machine Learning Salary and Job Outlook

Machine learning PhD graduates earn a highly favorable salary because a PhD is the highest degree level someone can earn. As stated above, PayScale does not list the average salary of a machine learning PhD graduate, but it notes that the average salary of an AI PhD graduate is  $115,000. Artificial intelligence is a field very closely related to machine learning.

The job outlook for a machine learning PhD graduate is between 12 and 22 percent. That is a very favorable job outlook. The BLS has stated that there are approximately 33,000 machine learning jobs each year.

What Can You Do With a PhD in Machine Learning?

With a PhD in machine learning, you can become a computer and information research scientist, a deep learning research engineer, or a computational linguist. Most higher education institutions offer career coaching services that help students prepare for interviews, write resumes, and find jobs. Contact your college to find out whether it offers career services.

Best Jobs with a PhD in Machine Learning

• Computer and Information Research Scientist
• Machine Learning Engineer
• Deep Learning Research Engineer
• Professor of Machine Learning
• Computational Linguist

What Is the Average Salary for a PhD in Machine Learning?

The average salary for a PhD in machine learning is $115,000 per year . This is a high average salary, but it varies based on factors such as experience, location, and job description. The more experience you have and the higher your degree level is, the higher your salary will be. If you decide to become a computer and information research scientist, you can earn an average salary of $131,490. If you are part of the 90th percentile, you can earn more than $208,000 annually .

Highest-Paying Machine Learning Jobs for PhD Grads

Best Machine Learning Jobs with a Doctorate

Now that we have looked at all the details about a machine learning PhD and how to become a machine learning engineer , let’s look at the five highest-paying machine learning Jobs for PhD graduates, in detail.

A machine learning engineer develops artificial intelligence systems that research and create algorithms that use large datasets. These algorithms can learn and make accurate predictions. Machine learning engineers are very skilled at programming, and they use programming languages like Java and Python.

• Salary with a Machine Learning PhD: $112,513
• Job Outlook: 22% job growth from 2020 to 2030
• Number of Jobs: 33,000
• Highest-Paying States: Oregon, Arizona, Texas, Massachusetts, Washington

Deep learning research engineers use deep learning platforms to create programming systems that copy brain functions. They do this using neural networks, which have a similar structure to the human brain. These programming systems are designed to learn without the help of humans.

• Salary with a Machine Learning PhD: $110,679

A computer and information research scientist improves and creates computer hardware and software using complex algorithms. They streamline these complex algorithms and enhance system efficiency. Computer and information research scientists' simplified algorithms lead to advancements in machine learning systems and other types of technology.

• Salary with a Machine Learning PhD: $100,384

Professors of machine learning usually teach students at a university or college. They will teach courses related to a specific field. In this case, they will teach courses related to machine learning. Professors at big institutions may also conduct research and experiments and publish original research. If you enjoy teaching you can become a professor of machine learning. 

• Salary with a Machine Learning PhD: $98,500
• Job Outlook: 12% job growth from 2020 to 2030
• Number of Jobs: 1,276,900
• Highest-Paying States: Alaska, New York, Utah, California, New Jersey

Computational linguists are a specific kind of computer scientist. They work with computers and teach computer systems how to understand human languages. They have excellent coding skills because they use programming languages to code. They also conduct computational linguistic research around a specific functional area or product line.

• Salary with a Machine Learning PhD: $80,330

Is a PhD in Machine Learning Worth It?

Yes, a PhD in Machine Learning is worth it. There are many excellent institutions that can help you earn a PhD in Machine Learning while providing valuable support from faculty members. Earning this type of degree can help you further your machine learning career.

If you pursue a PhD in machine learning, you will very likely add value to your industry with the research conducted during your dissertation. Completing a PhD takes many years and is research-intensive but completely worth it if you look at the jobs that use machine learning and the average PhD in Machine learning salary.

Additional Reading About Machine Learning

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PhD in Machine Learning FAQ

The cheapest PhD in machine learning is the PhD in Machine Learning and Data Science offered by University of California San Diego. The PhD in Machine Learning and Data Science tuition at University of California San Diego costs $11,442 per year for both residents and non-residents.

Many top companies hire machine learning PhD graduates, including Google, Microsoft, Adobe, PayPal, Amazon, IBM, and Duolingo. With a PhD in machine learning, you can land a job at one of these companies and earn a high salary.

Yes, there are many remote jobs available for machine learning graduates. A quick search on websites such as Indeed, Glassdoor, and LinkedIn can put you in touch with many possible machine learning jobs. Make sure you read the details of each job carefully before you apply.

Yes, you can get a job in machine learning with a bootcamp. Bootcamps are short, but they are  intensive and can teach you all the necessary skills to have a successful career in the machine learning industry. There are many excellent machine learning bootcamps to help you start your machine learning career.

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