AIML Courses at Boston University
A list of AIML courses at Boston University.
For the broader departmental lists, see Computer Science Courses at BU, Data Science Courses at BU, and Linguistics Courses at BU. Courses I’ve taken are recorded in BU Coursework.
H2 Computing and Data Sciences
- CDS DS 340 - Introduction to Machine Learning and AI
- CDS DS 444 / 644 - Machine Learning for Business Analytics
- CDS DS 480 / 680 - Data, Society, and AI Ethics
- CDS DS 542 - Deep Learning for Data Science
- CDS DS 543 - Introduction to Reinforcement Learning
- CDS DS 592 - Special Topics in Mathematical and Computational Sciences
- Introduction to Sequential Decision Making (Spring 2025)
- CDS DS 593 - Special Topics in Data Science Methodologies
- Theory and Applications of Large Language Models (Spring 2026)
- CDS DS 598 - Special Topics in Machine Learning
- Introduction to Reinforcement Learning (Spring 2024)
- Deep Learning for Data Science (Spring 2024)
H2 Computer Science
- CAS CS 440 - Introduction to Artificial Intelligence
- CAS CS 505 - Introduction to Natural Language Processing
- CAS CS 523 - Deep Learning
- CAS CS 541 - Applied Machine Learning
- CAS CS 542 - Principles of Machine Learning
- CAS CS 585 - Image and Video Computing
- CAS CS 599 - Advanced Topics in Computer Science
- Multimodal Machine Learning (Spring 2021, Spring 2025, Spring 2026)
- Privacy in Statistics and Machine Learning (Spring 2021, Spring 2023, Spring 2025)
- Algorithms for Machine Learning (Fall 2025)
- Advanced NLP Seminar: Interpretable Machine Learning (Fall 2025)
- Advanced Topics in Computer Vision (Fall 2025)
- Applied Machine Learning (Fall 2022)
- AI Agents and Human-AI Interaction (Fall 2025)
- GRS CS 640 - Artificial Intelligence
H2 Linguistics
- CAS LX 390 / GRS LX 690 / MET LX 590 - Topics in Linguistics
- Metrics and Evaluation in Natural Language Processing (Spring 2023, Spring 2024)
- Contemporary Research in Neural Network Models of Language (Fall 2025)
- Large Language Models (Spring 2026)
- CAS LX 394 / GRS LX 694 / MET LX 594 - Introduction to Programming for Computational Linguistics
- CAS LX 496 / GRS LX 796 / MET LX 596 - Computational Linguistics
H2 Mathematics and Statistics
- CAS MA 679 - Applied Statistical Machine Learning
- CAS MA 751 - Statistical Machine Learning
H2 Electrical and Computer Engineering
- ENG EC 414 - Introduction to Machine Learning
- ENG EC 418 - Introduction to Reinforcement Learning
- ENG EC 503 - Introduction to Learning from Data
- ENG EC 523 - Deep Learning
- ENG EC 525 - Optimization for Machine Learning
H2 Biomedical Engineering
- ENG BE 559 - Foundations of Biomedical Data Science and Machine Learning
- ENG BE 562 - Computational Biology: Machine Learning Fundamentals
H2 Bioinformatics
- CDS BF 550 - Foundations of Programming, Data Analytics, and Machine Learning in Python
H2 Questrom School of Business
- QST BA 576 - Machine Learning for Business Analytics
- QST BA 810 - Supervised Machine Learning
- QST BA 820 - Unsupervised and Unstructured Machine Learning
- QST MF 815 - Advanced Machine Learning Applications for Finance
- QST MF 850 - Deep Learning, Statistical Learning
- QST MK 842 - Machine Learning for Business Analytics
- QST IS 883 - Deploying Generative AI in the Enterprise
- QST IS 911 - Generative AI & Causal Inference with Text
H2 Writing Program
- CAS WR 152 - Writing, Research, & Inquiry with Digital/Multimedia Expression
- The Philosophy and Ethics of Artificial Intelligence
- CAS WR 153 - Writing, Research, & Inquiry with Creativity/Innovation
- The Philosophy and Ethics of Artificial Intelligence (Summer 2025)
- CAS WR 250 - AI Literacy for Writing
H2 Computing and Data Sciences (Online)
- CDS DX 603 - Machine Learning Fundamentals
- CDS DX 699 - AI for Leaders
- CDS DX 703 - Advanced Machine Learning & AI
- CDS DX 704 - AI in the Field