As of Fall 2025, Data Science students are permitted to choose 1 of the following 2 courses to satisfy the introduction to data science requirement.  *601.675 will no longer be permitted to satisfy the machine learning core requirement.

EN.553.636 Introduction to Data Science Fall, Spring
EN.601.675* Machine Learning Fall, Spring

Course # Course Title Semester “Typically” Offered** Online? Core Area Notes
EN.520.612 Machine Learning for Signal Processing Spring Machine Learning
EN.520.617 Computation for Engineers Spring Computing
EN.520.618 Modern Convex Optimization Fall Optimization
EN.520.637 Foundations of Reinforcement Learning Fall Machine Learning
EN.520.638 Deep Learning Spring Machine Learning
EN.520.647 Information Theory Spring Machine Learning
EN.520.648 Compressed Sensing and Sparse Recovery Spring Machine Learning
EN.520.651 Random Signal Analysis Fall Machine Learning
EN.520.666 Information Extraction Spring Machine Learning
EN.525.724 Introduction to Pattern Recognition Fall YES Machine Learning
EN.530.641 Statistical Learning for Engineers Fall Machine Learning
EN.535.741 Topics in Data Analysis Spring Machine Learning
EN.553.602 Research and Design in Applied Mathematics:  Data Mining Fall, Spring Machine Learning
EN.553.613 Applied Statistics and Data Analysis Fall, Spring, Summer Statistics
EN.553.614 Applied Statistics and Data Analysis II Spring Statistics
EN.553.630 Introduction to Statistics Fall, Spring, Summer Statistics
EN.553.632 Bayesian Statistics Fall, Spring, Summer Statistics
EN.553.639 Time Series Analysis Spring Statistics
EN.553.653 Mathematical Game Theory Spring Optimization
EN.553.661 Optimization in Finance Fall Optimization
EN.553.662 Optimization in Data Science Spring Optimization
EN.553.665 Introduction to Convexity Fall, Spring Optimization
EN.553.669 Large-Scale Optimization for Data Science Optimization
EN.553.688 Computing for Applied Mathematics Fall, Spring Computing
EN.553.724 Probabilistic Machine Learning Spring Machine Learning
EN.553.730 Statistical Theory I Fall, Spring, Summer Statistics
EN.553.731 Statistical Theory II Spring Statistics
EN.553.733 Advanced Topics in Bayesian Analysis Fall Statistics
EN.553.735 Topics in Statistical Pattern Recognition Statistics
EN.553.738 High-Dimensional Approximation, Probability, and Statistical Learning Statistics, Machine Learning
EN.553.739 Statistical Pattern Recognition Theory & Methods Spring Statistics
EN.553.740 Machine Learning I Fall Machine Learning
EN.553.741 Machine Learning II Spring Machine Learning
EN.553.743 Equivariant Machine Learning Spring Machine Learning
EN.553.761 Nonlinear Optimization I Fall Optimization
EN.553.762 Nonlinear Optimization II Spring Optimization
EN.553.763 Stochastic Search and Optimization Optimization
EN.553.766 Combinatorial Optimization Spring Optimization
EN.553.797 Introduction to Control Theory and Optimization Control Spring Optimization
EN.570.654 Geostatistics:  Understanding Spatial Data Statistics, Machine Learning(1) Not offered since S21.
EN.601.619 Cloud Computing Computing
EN.601.620 Parallel Programming Fall Computing
EN.601.633 Intro Algorithms Fall, Spring Computing
EN.601.634 Randomized and Big Data Algorithms Spring Machine Learning
EN.601.646 Sketching and Indexing for Sequence Spring Computing
EN.601.647 Computation Genomics: Sequences Fall Computing
EN.601.674 Machine Learning: Learning Theory Fall Machine Learning
EN.601.675 Machine Learning Fall, Spring Machine Learning
EN.601.676 Machine Learning: Data to Models Machine Learning
EN.601.677 Causal Inference Statistics, Machine Learning(1)
EN.601.682 Machine Learning: Deep Learning Fall, Spring Machine Learning
EN.601.779 Machine Learning: Advanced Topics Machine Learning
EN.601.780 Unsupervised Learning: Big Data to Low-Dimensional Representations Machine Learning
EN.625.603 Statistical Methods and Data Analysis Fall, Spring, Summer YES Statistics
EN.625.615 Introduction to Optimization Fall, Spring YES Optimization
EN.625.664 Computational Statistics Fall, Spring YES Statistics, Computing
EN.625.692 Probabilistic Graphical Models Spring YES Machine Learning
EN.685.621 Algorithms for Data Science Fall, Spring, Summer YES Computing
Course schedules change. Always check SIS.
(1) As of Fall 2025, students can no longer take these courses to satisfy the machine learning core requirement.

Course # Course Title Semester “Typically” Offered Online? Elective Focus Area NOTES
AS.410.633 Introduction to Bioinformatics Fall, Spring Computational Medicine
AS.410.635 Bioinformatics: Tools for Genome Analysis Fall, Spring Computational Medicine
AS.410.671 Gene Expression Data Analysis and Visualization Fall, Summer Computational Medicine
EN.520.612 Machine Learning for Signal Processing Fall
EN.520.614 Image Processing and Analysis Fall, Spring Computer Vision
EN.520.615 Image Processing and Analysis II Spring Computer Vision
EN.520.617 Computation for Engineers Spring
EN.520.618 Modern Convex Optimization Fall
EN.520.623 Medical Image Analysis Spring Computer Vision
EN.520.635 Digital Signal Processing Fall Computer Vision
EN.520.637 Foundations of Reinforcement Learning Fall
EN.520.638 Deep Learning Spring
EN.520.640 Machine Intelligence on Embedded Systems Spring
EN.520.646 Wavelets & Filter Banks Fall Computer Vision
EN.520.647 Information Theory Fall Removed as core November 2024. Any student who successfully completed this course prior to S25, may count this as Machine Learning Core.
EN.520.648 Compressed Sensing and Sparse Recovery Spring Computer Vision
EN.520.650 Machine Intelligence Spring
EN.520.651 Random Signal Analysis Fall
EN.520.659 Machine Learning for Medical Application Spring Computational Medicine
EN.520.661 AI and Biometric Systems: Techniques, Applications, and Ethics Spring
EN.520.665 Machine Perception Fall
EN.520.666 Information Extraction Spring Language and Speech
EN.520.680 Speech and Auditory Processing by Humans and Machines Spring Language and Speech
EN.525.724 Introduction to Pattern Recognition Fall YES
EN.525.733 Deep Learning for Computer Vision Spring YES Computer Vision
EN.530.641 Statistical Learning for Engineers Fall
EN.535.741 Topics in Data Analysis Spring YES
EN.553.602 Research and Design in Applied Mathematics:  Data Mining Spring
EN.553.613 Applied Statistics and Data Analysis Fall
EN.553.614 Applied Statistics and Data Analysis II Spring
EN.553.627 Stochastic Processes in Finance I Fall Mathematical Finance
EN.553.628 Stochastic Processes in Finance II Spring Mathematical Finance
EN.553.630 Mathematics Statistics (formerly Introduction to Statistics) Fall, Spring
EN.553.632 Bayesian Statistics Fall
EN.553.633 Monte Carlo Methods Fall Mathematics of Data Science
EN.553.635 Mathematical Game Theory Spring
EN.553.639 Time Series Analysis Spring
EN.553.641 Equity Markets and Quantitative Trading Spring Mathematical Finance
EN.553.642 Investment Science Fall Mathematical Finance
EN.553.644 Introduction to Financial Derivatives Fall Mathematical Finance
EN.553.645 Interest Rate and Credit Derivatives Spring Mathematical Finance
EN.553.646 Risk Measurement and Management in Financial Markets Fall Mathematical Finance
EN.553.647 Quantitative Portfolio Theory & Performance Analysis Spring Mathematical Finance
EN.553.648 Financial Engineering and Structured Products Spring Mathematical Finance Course no longer offered.
EN.553.649 Advanced Equity Derivatives Fall Mathematical Finance
EN.553.650 Computational Molecular Medicine Spring Computational Medicine
EN.553.653 Mathematical Game Theory Spring
EN.553.661 Optimization in Finance Fall
EN.553.662 Optimization in Data Science Spring
EN.553.665 Introduction to Convexity Fall Removed as CORE but permitted as elective.
EN.553.669 Large-Scale Optimization for Data Science Fall
EN.553.688 Computing for Applied Mathematics Fall, Spring
EN.553.689 Software Engineering for Data Science Fall
EN.553.693 Mathematical Image Analysis Spring Computer Vision
EN.553.724 Probabilistic Machine Learning Spring
EN.553.730 Statistical Theory I Fall
EN.553.731 Statistical Theory II Spring
EN.553.733 Advanced Topics in Bayesian Analysis Spring
EN.553.735 Topics in Statistical Pattern Recognition Fall
EN.553.738 High-Dimensional Approximation, Probability, and Statistical Learning Spring Mathematics of Data Science
EN.553.739 Statistical Pattern Recognition Theory & Methods Spring
EN.553.740 Machine Learning I Fall Mathematics of Data Science
EN.553.741 Machine Learning II Spring Mathematics of Data Science
EN.553.743 Equivariant Machine Learning Fall Mathematical Finance
EN.553.744 Data Science Methods for Large Scale Graphs Spring
EN.553.753 Commodities and Commodity Markets Spring Mathematical Finance
EN.553.761 Nonlinear Optimization I Fall
EN.553.762 Nonlinear Optimization II Spring
EN.553.763 Stochastic Search and Optimization Spring
EN.553.766 Combinatorial Optimization Spring
EN.553.792 Matrix Analysis Fall Mathematics of Data Science
EN.553.797 Introduction to Control Theory and Optimization Control Spring
EN.553.806 Capstone Experience Fall, Spring, Summer
EN.570.654 Geostatistics:  Understanding Spatial Data Spring As of S25, course being removed from ML Core, but kept as Statistics core. Any student who successfully completed this course prior to S25, may count this as a Statistics core course.
EN.580.627 Deep Learning for Medical Imaging Spring Computer Vision Added 3/31/25
EN.580.688 Foundations of Computational Biology and Bioinformatics Spring Computational Medicine
EN.580.691 Learning, Estimation and Control Spring
EN.580.709 Sparse Representations in Computer Vision and Machine Learning Fall
EN.601.615 Databases Fall
EN.601.619 Cloud Computing Spring
EN.601.620 Parallel Programming Fall
EN.601.633 Intro Algorithms Fall, Spring
EN.601.634 Randomized and Big Data Algorithms Fall Mathematics of Data Science
EN.601.635 Approximation Algorithms Spring
EN.601.637 Federated Learning an Analytics Fall
EN.601.641 Blockchains and Cryptocurrencies Fall, Spring Approved, but not currently offered.
EN.601.642 Modern Cryptography Fall
EN.601.646 Sketching and Indexing for Sequence Spring
EN.601.647 Computation Genomics: Sequences Fall
EN.601.651 Introduction to Computational Immunogenetics Fall Computational Medicine
EN.601.661 Computer Vision Fall, Spring Computer Vision
EN.601.663 Algorithms for Sensor-Based Robotics Fall, Spring
EN.601.664 Artificial Intelligence Spring
EN.601.665 Natural Language Processing Fall Language and Speech Not permitted as Optimization Core, but remains as Natural Language Processing elective.
EN.601.666 Information Retrieval and Web Agents Spring
EN.601.668 Machine Translation Fall Language and Speech
EN.601.670 Artificial Agents Fall Language and Speech
EN.601.671 Natural Language Processing:  Self-Supervised Models Spring Language and Speech Not permitted as Core, but permitted as Elective.
EN.601.674 Machine Learning: Learning Theory Fall
EN.601.676 Machine Learning: Data to Models Spring
EN.601.677 Causal Inference Fall
EN.601.682 Machine Learning: Deep Learning Fall
EN.601.690 Introduction to Human-Computer Interaction Spring
EN.601.765 Machine Learning: Linguistic and Sequence Modeling N/A Not offered since Spring 2019.
EN.601.769 Event Semantics in Theory and Practice Spring Language and Speech
EN.601.773 Machine Social Intelligence Spring Added 3/31/25
EN.601.779 Machine Learning: Advanced Topics Spring
EN.601.780 Unsupervised Learning: Big Data to Low-Dimensional Representations Fall
EN.601.783 Vision as Bayesian Inference Spring Computer Vision
EN.601.788 Machine Learning for Healthcare Fall
EN.605.620 Algorithms for Bioinformatics Fall, Spring YES Computational Medicine Cannot be taken with EN.605.621.
EN.605.621 Foundations of Algorithms Fall, Spring YES Computational Medicine Cannot be taken with EN.605.620.
EN.605.626 Image Processing Fall YES Computer Vision
EN.605.653 Computational Genomics Fall YES Computational Medicine
EN.625.603 Statistical Methods and Data Analysis Fall, Spring, Summer YES
EN.625.615 Introduction to Optimization Fall, Spring YES
EN.625.664 Computational Statistics YES
EN.625.692 Probabilistic Graphical Models Spring YES
EN.650.683 Cybersecurity Risk Management Fall
EN.685.621 Algorithms for Data Science Spring, Summer YES
Note that course schedules change periodically. AMS has no control over courses offered by other departments (when they are offered, restrictions on courses, etc.)

All students must complete a final capstone project worth 6 credits. For details and instructions, click here.

The following courses were previously requested to be added to the data science approved courses list. They were reviewed by the committee and ultimately the decision was made to NOT APPROVE them for the data science master’s program.

Course # Course Title
EN.553.620 Introduction to Probability
EN.553.767 Iterative Algorithms in Machine Learning
EN.605.608 Software Project Management
EN.605.631 Statistical Methods for Computer Science
EN.605.635 Cloud Computing
EN.625.604 Ordinary Differential Equations
EN.625.609 Matrix Theory
EN.625.636 Graph Theory
EN.705.601 Applied Machine Learning

  1. Before requesting a course be considered for the approved courses list, ALWAYS check to ensure that the course has not already been reviewed and rejected. You can find this above under the Reviewed but NOT approved accordion.
  2. Obtain a copy of the course syllabus from the instructor. The description in SIS is not enough for the committee to decide. They need the entire syllabus.
  3. Send the syllabus to your FACULTY advisor. If he or she believes the course has merit, then upload the course syllabus to the link below.
  4. Upload the syllabus – https://forms.office.com/r/stXXWxwWAZ.

 

Courses offered by schools other than EN (Engineering) or Arts & Sciences (AS) must be registered for by using the Registrar’s Interdivisional Registration form (IDR).  This also includes courses offered by Engineering for Professionals, which are mostly online courses. You will know it’s an EP course because the first 3 numbers will end in 5. For example – 

EN.535.XXX (mechanical engineering)
EN.605.XXX (computer science)
EN.525.XXX (electrical and computer engineering)

THIS FORM CAN BE DOWNLOADED FROM THE REGISTRAR’S WEBSITE and will require your advisor’s signature.  The signed form must then be submitted to SEAM.

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