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MEng: Artificial Intelligence (AI)

In this age of AI explosion, come learn in the place where "AI" was coined. This track is for engineers who want a rigorous understanding of the foundations of AI: to know where to use AI and use it appropriately, to recognize and anticipate the impacts of AI—both good and bad, and to be an AI architect who can not only see the big picture but also drill down and assess the different paths to the future. Ultimately, the goal is to be able to not just synthesize, but to create new AI solutions for the benefit of society.

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Course Requirements

The program consists of nine courses, of which seven should be from the list of core courses. The remaining two electives can consist of any graduate-level engineering or science courses at Dartmouth.

During the student's first term, they will work with advisors to develop a full program plan to fulfill the MEng requirements, which they will submit to the Graduate Academic Advising Director.

Please note: The information below reflects degree requirements, effective as of winter 2026.

COURSE TYPE	REQUIRED NUMBER OF COURSES	COURSES
Core Courses	7 courses	Choose 7 courses, from the following: ENGS 96: Math for Machine Learning ENGS 101: Principles of Reinforcement Learning ENGS 102: Game-theoretic Design, Learning, and Engineering ENGS 106: Principles of Machine Learning ENGS 108: Applied Machine Learning* ENGS 109: High-dimensional Sensing and Learning (HdSL) ENGS 177: Decision-Making under Uncertainty ENGG 199.19: Principles of Causality
Electives**	2 courses	Choose 2 courses, from the following: Engineering Any graduate-level engineering sciences courses. Design Project Students may take one of the following options to partially fulfill the two-course elective requirement. The appropriate option will depend on each student's prior experience and coursework since the two tracks require different prerequisites. Option 1 (1 course) ENGM 191: Product Design and Development Option 2 (2 courses) ENGS 190: Engineering Design and Methodology Project Initiation ENGS 290: Engineering Design Methodology and Project Completion Science Any graduate-level science courses.

COURSE TYPE

REQUIRED NUMBER OF COURSES

COURSES

Core Courses

7 courses

Choose 7 courses, from the following:

ENGS 96: Math for Machine Learning
ENGS 101: Principles of Reinforcement Learning
ENGS 102: Game-theoretic Design, Learning, and Engineering
ENGS 106: Principles of Machine Learning
ENGS 108: Applied Machine Learning*
ENGS 109: High-dimensional Sensing and Learning (HdSL)
ENGS 177: Decision-Making under Uncertainty
ENGG 199.19: Principles of Causality

Electives**

2 courses

Choose 2 courses, from the following:

Engineering

Any graduate-level engineering sciences courses.

Design Project

Students may take one of the following options to partially fulfill the two-course elective requirement. The appropriate option will depend on each student's prior experience and coursework since the two tracks require different prerequisites.

Option 1 (1 course)

ENGM 191: Product Design and Development

Option 2 (2 courses)

ENGS 190: Engineering Design and Methodology Project Initiation
ENGS 290: Engineering Design Methodology and Project Completion

Science
Any graduate-level science courses.

* ENGS 108, COSC 274, and QBS 108 have overlapping material, and only one may be taken for credit.

** Students may choose electives from any graduate-level engineering or science courses offered at Dartmouth. The courses must be approved in advance by the MEng Faculty Director. With the exception of ENGM 191: Product Design and Development, MEng students may not take ENGM courses for credit.