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.

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 nine 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.

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

The online MEng: CE requires a total of nine courses, including a capstone. 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, Signal Processing, Machine Learning, and Embedded Systems must be taken early in the program. The capstone course, Smart Sensors, must be taken last.)

Extracting Information from Data

  • Machine Learning (must be taken early)

    In this detailed overview, you'll gain a deeper understanding of machine learning, laying the foundations for other courses in the program. With a heavy emphasis on practical application, this course will provide you with essential data cleaning preparation techniques, along with logistic regression, foundational statistics, decision trees, and preliminary exploration of neural networks. This course will be primarily taught using Python, with some additional use of MATLAB.

  • Signal Processing (must be taken early)

    In this course, you'll take the mathematical theories that underpin the discipline of signal processing, and use them in applied settings, allowing you to analyze, optimize, and adjust a wide range of data types.

    You'll learn about the use of:

    • Signal filtering
    • De-noising
    • Signal to noise ratio
    • Signal enhancement
    • Data compression
    • Feature extraction
    • Feature engineering
    • Discrete and fast Fourier Transform
    • Random signals
    • Probability distributions
  • Applied Natural Language Processing

    Building on the knowledge gained through the Signal Processing and Machine Learning courses, here you'll learn the basics of natural language processing (NLP)—the linguistic theories underpinning it, the techniques and challenges that define the NLP landscape, and both the current and developing tools used to implement it. You'll also gain a deeper understanding of the principles governing the development of generative AI models.

    Prerequisites: Signal Processing, Machine Learning

  • Machine Vision

    In this course, you'll take concepts of machine learning and signal processing learned earlier in the program, and learn how these tools can be used to allow computers to extract high-level understanding from visual images. You'll trace the development of machine vision capabilities, from traditional machine vision tools through to the latest neural network algorithm functionality.

    Prerequisites: Signal Processing, Machine Learning

  • Deep Learning for Sensor Data

    This course focuses on the challenges and methods involved in processing sensor data as it streams, as opposed to static datasets. You'll learn about the ways that streaming data is pre-processed, filtered and interpreted, and how cumulative meaning and context can be continually extracted from the data stream. You'll learn about the specific types of neural networks used to process this kind of data, and the real-world challenges such as latency that affect how we use sensors.

    Prerequisites: Signal Processing, Machine Learning

Hardware for Intelligent Systems

  • Embedded Systems (must be taken early)

    You'll learn about the different types of hardware platforms, software tools, and techniques used in the design of intelligent systems. Focusing particularly on the application of microcontrollers, you'll learn how to design, program, test, and debug embedded systems. You'll develop hardware-level device drivers for connected sensors/actuators, implement real-time data processing and control algorithms, and work with communications interfaces.

    Prerequisites: C is the primary language used in microcontroller programming. Students should be familiar with the C language and, in particular, with C pointers and structures. Microcontrollers are programmed "at the hardware level"; students should be familiar with basic digital logic concepts including digital I/O, discrete logic gates (AND, OR, NOT), registers, Boolean/hexadecimal number representation, and Boolean arithmetic operations (add, multiply, negate).

  • FPGA Architecture and Algorithms

    In this course, you'll learn how to use FPGA architecture and algorithms for deep neural network learning. You'll gain an overview of the specialized hardware devices being used to implement deep neural networks across a broad range of industries and applications, and why FPGA systems are the natural choice in many of these instances.

  • Distributed Computing*

    In this course, you'll learn how different code needs to be implemented and executed across a variety of platforms, keeping in mind the different capabilities of these platforms, their requirements, and their limitations.

    * This course may be taken concurrently with the capstone course.

Capstone

  • Smart Sensors (must be taken last)

    In this final course, you'll apply everything you've learned and work with your peers on a larger-scale, 'Smart Sensors' project. Your instructors will aim to scaffold your learning by breaking down the project into stages, based on the different subject areas you've already covered. Previous 'Smart Sensors' projects have required students to plan, design and create a mobile sensor device for biomedical application, incorporating multiple course threads such as signal processing, sensor data processing, and NLP keyword processing.

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 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 Continuous Enrollment Starting Spring Term (27 months)

Year 1

Term 1
Machine Learning

Term 2
Embedded Systems

Term 3
Signal Processing

Term 4
FPGA Architecture and Algorithms

Year 2

Term 1
Distributed Computing

Term 2
Applied Natural Language Processing

Term 3
Deep Learning for Sensor Data

Term 4
Computer Vision

Year 3

Term 1
Smart Sensors (Capstone)

Full-Time Continuous Enrollment Starting Spring Term (15 months)

Year 1

Term 1
Machine Learning
Signal Processing

Term 2
Embedded Systems
Natural Language Processing

Term 3
Deep Learning

Term 4
FPGA Architecture
Computer Vision

Year 2

Term 1
Distributed Computing
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

Jason Dahlstrom

Jason Dahlstrom
Lecturer