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Online MEng: Computer Engineering Academic Overview

Dartmouth's online Master of Engineering in Computer Engineering (MEng: CE) courses are designed to reflect both current and emerging engineering challenges in industry. You'll learn fundamental concepts in computer engineering and intelligent systems, as well as gain hands-on project experience that mirrors the real-world.

The online MEng in Computer Engineering focuses on intelligent systems — machines that interact with the world via a combination of sensing, computing, and actuation. Students in this program will learn to engineer the sensing and computing components of intelligent systems.

Learning Experience

The online MEng: CE leverages Coursera's online education platform to deliver the curriculum, allowing you to benefit from interactive video transcription, in-course note taking, and seamless learning across multiple devices—at a schedule and pace that best fits in your life. Online courses include readings, video lectures, assignments, and discussion forums that help spark connections with your peers.

Study on Your Own Schedule

Dive deep into each course with pre-recorded, high quality video lectures, and get your work done on a schedule most convenient for you.

Personal Support from Professors

Ask questions and get one-on-one support and guidance during virtual office hours from your faculty and teaching assistants.

Peers from Around the World

Learn alongside online peers who bring their global perspectives and unique experiences to each course, and connect in online forums and channels.

Learning Objectives

You'll learn to engineer the sensing and computing components of intelligent systems through a series of 9 carefully curated courses, including a capstone. Designed and taught by cross-disciplinary faculty and industry leaders, the curriculum will immerse you in the knowledge and skills necessary to drive the next generation of computer engineering and technology, including virtual/augmented reality, autonomous robots, self-driving cars, AI virtual assistants, wearable/implantable devices, and more.

Through the program, you'll learn to:

  • Extract information from data, using a combination of broadly-applicable tools and task-specific techniques such as signal processing, machine learning, and machine vision.
  • Implement information-extracting algorithms that fit within the constraints—and utilize the capabilities—of specialized computer hardware for intelligent systems.
  • Design, analyze, build, test, and debug sensing and computing components of intelligent systems.
  • Collaborate on projects with geographically-diverse team members.

Required Courses

Please note: The information below reflects degree requirements, effective as of Fall 2025.

The online MEng: CE requires a total of nine courses, including a capstone course. Students may take one or two courses at a time (two courses is considered a full-time course load). While the order in which most courses are taken is flexible, some courses serve as prerequisites to more advanced courses that require them to be taken early on. For example, ENGG 408: Machine Learning and ENGG 410: Signal Processing must be taken early in the program. The capstone course, ENGG 499: Smart Sensors, must be taken in your final term of enrollment.

The nine courses fall into the following broad groups:

Extracting Information from Data

  • ENGG 408: Machine Learning (must be taken early)
  • ENGG 410: Signal Processing (must be taken early)
  • ENGG 418: Applied Natural Language Processing
  • ENGG 417: Machine Vision
  • ENGG 419: Deep Learning

Hardware for Intelligent Systems

Capstone

  • ENGG 499: Smart Sensors (must be taken in final term)

Sample Course Plans

These sample course plans provide examples of how you might progress through the program either part-time or full-time with a Fall or Spring term start. You do not need to follow these plans exactly—your schedule may be different depending on balancing other responsibilities. Once enrolled, you'll be given a degree path planner to help guide your journey through the program. Program staff are available for any assistance along the way.

Part-time Option, with Continuous Enrollment: Fall Term Start (27 months)

 FallWinterSpringSummer
Year 1ENGG 408 
Machine Learning
ENGG 463 
Advanced FPGA Design
ENGG 410 
Signal Processing
ENGG 419 
Deep Learning
Year 2ENGG 418 
Natural Language Processing
ENGG 415
Distributed Computing
ENGG 417 
Machine Vision
ENGG 462 
Embedded Systems
Year 3ENGG 499
Smart Sensors (Capstone)
   

Full-time Option, with Continuous Enrollment: Fall Term Start (15 months)

 FallWinterSpringSummer
Year 1

ENGG 408 
Machine Learning

ENGG 410 
Signal Processing

ENGG 463 
Advanced FPGA Design

ENGG 415
Distributed Computing

ENGG 417 
Machine Vision

ENGG 462 
Embedded Systems

ENGG 419 
Deep Learning

Year 2

ENGG 418 
Natural Language Processing

ENGG 499 
Smart Sensors (Capstone)

   

Part-time Option, with Continuous Enrollment: Spring Term Start (27 months)

 SpringSummerFallWinter
Year 1ENGG 408 
Machine Learning
ENGG 462 
Embedded Systems
ENGG 410 
Signal Processing
ENGG 463 
Advanced FPGA Design
Year 2ENGG 417 
Machine Vision
ENGG 419 
Deep Learning
ENGG 418 
Natural Language Processing
ENGG 415 
Distributed Computing
Year 3ENGG 499 
Smart Sensors (Capstone)
   

Full-time Option, with Continuous Enrollment: Spring Term Start (15 months)

 SpringSummerFallWinter
Year 1

ENGG 408 
Machine Learning

ENGG 410 
Signal Processing

ENGG 462 
Embedded Systems

ENGG 419 
Deep Learning

ENGG 418 
Natural Language Processing

ENGG 463 
Advanced FPGA Design

ENGG 415
Distributed Computing

Year 2

ENGG 417 
Machine Vision

ENGG 499 
Smart Sensors (Capstone)

   

Faculty

Eugene Santos,Jr.

Eugene Santos Jr.
Professor of Engineering
Faculty Director, Master of Engineering Program

Kofi M. Odame

Kofi M. Odame
Associate Professor of Engineering
Program Area Lead, Electrical and Computer Engineering

Peter Chin

Peter Chin
Professor of Engineering

Kelly Seals, professor of engineering

Kelly Seals
Professor of Engineering

Kendall Farnham, Assistant Professor of Engineering

Kendall Farnham
Assistant Professor of Engineering

Michael Kokko Assistant Professor of Engineering Director, Instructional Labs

Michael Kokko
Assistant Professor of Engineering
Director, Instructional Labs

Professors Jason Dahlstrom

Jason Dahlstrom
Adjunct Assistant Professor of Engineering

Tucker "Emme" Burgin
Assistant Professor of Engineering