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Compare official core options, published offerings, prerequisites, and instructors across all seven on-campus tracks.
AI
SELECTED SPECIALTY
Artificial Intelligence
Requirements effective Winter 20267core courses
2electives
8listed options
9courses total
CORE COURSE CATALOG
Compare your core options
OFFERED
| Course | Published sections | Prerequisites |
|---|---|---|
| ENGS 96Math for Machine LearningDevelops practical methods for learning patterns from data, evaluating models, and applying them to engineering decisions. | F26 · Meeting slot 11 Bruno Miranda HenriqueRMP ↗ | ENGS 20 ↗ or COSC 10 ↗, and MATH 8 ↗. MATH 20 ↗ and MATH 22 ↗ are recommended but not mandatory. |
| ENGS 101Principles of Reinforcement LearningStudies how agents learn sequential decisions from rewards, including value functions, policies, and modern learning algorithms. | F26 · Meeting slot 10 Peter ChinRMP ↗ | Multivariable calculus (MATH 8 ↗ or MATH 9 ↗); Linear algebra (MATH 22 ↗ or MATH 24 ↗); Probability (MATH 20 ↗, ENGS 93 ↗, or ENGG 193 ↗); and ENGS 20 ↗ or COSC 10 ↗. ENGS 96 ↗ is encouraged. |
| ENGS 102Game-theoretic Design, Learning and EngineeringUses strategic interaction, incentives, and learning dynamics to design and analyze engineered systems. | W27 · Meeting time TBA Bryce FergusonRMP ↗ | MATH 1 ↗ or 3, and MATH (8 or 9) or MATH 24 ↗, MATH 20 ↗ is a plus; and some level of proficiency in a programing language such as C/C++, Julia, Python, R, or MATLAB required |
| ENGS 105.1Principles of CausalityIntroduces causal graphs, interventions, and statistical methods for distinguishing cause-and-effect from correlation. | S27 · Meeting slot 11 Bijan MazaheriRMP ↗ | ENGS 20 ↗ or COSC 10 ↗, and ENGS 27 ↗ or ENGS 93 ↗; or permission of the instructor. |
| ENGS 106Principles of Machine LearningDevelops practical methods for learning patterns from data, evaluating models, and applying them to engineering decisions. | W27 · Meeting slot 10 Peter ChinRMP ↗ | Muti-variable calculus (MATH 8 ↗ or MATH 9 ↗), linear algebra (MATH 22 ↗ or MATH 24 ↗), and probability (MATH 20 ↗, ENGS 27 ↗, or ENGS 93 ↗) or equivalent. ENGS 96 ↗ encouraged. |
| ENGS 108Applied Machine LearningDevelops practical methods for learning patterns from data, evaluating models, and applying them to engineering decisions.Overlaps with COSC 274 and QBS 108; only one may be taken. | F26 · Meeting slot 12 George CybenkoRMP ↗ | ENGS 20 ↗ or equivalent, MATH 22 ↗ or equivalent, ENGS 27 ↗ or ENGS 93 ↗ or equivalent. |
| ENGS 109High-dimensional Sensing and Learning (HdSL)An advanced engineering course focused on the concepts, analytical tools, and practical applications of high-dimensional sensing and learning (hdsl). | S27 · Meeting slot 10 Peter ChinRMP ↗ | (MATH 8 ↗ or MATH 9 ↗) or (MATH 22 ↗ or MATH 24 ↗); MATH 20 ↗ is a plus; some proficiency of programing language (ENGS 20 ↗ or COSC 10 ↗) |
| ENGS 177Decision-Making under UncertaintyCovers probability, estimation, hypothesis testing, and statistical reasoning for engineering data and decisions. | S27 · Meeting slot 3A Wesley MarreroRMP ↗ | ENGS 103 ↗ or permission of the instructor. Additionally, students should be proficient in a programming language such as Julia, Python, R, or MATLAB. |
WHAT THIS DATA MEANS
A planning aid, not the registrar.
Core and elective guidance comes from Dartmouth Engineering’s track pages. Named elective recommendations are examples, not an exhaustive list. Terms, time blocks, prerequisites, and instructors come from the graduate engineering catalog’s published 2026–27 schedule. “Not yet published” does not mean a course is cancelled. Cross-listed COSC, PHYS, MATH, QBS, and BIOC courses should be verified in their home department.
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