Dartmouth Master of Engineering in Computer Engineering Course Overview V2

Interested in getting a closer look at Dartmouth's online Master of Engineering in Computer Engineering (MEng)? Get an inside scoop on the courses you can expect as a MEng student!

0:00 - Introduction

0:05 - Signal Processing
The mathematical theories that underpin the discipline of signal processing are presented and used in applied settings, allowing you to analyze, optimize, and adjust a wide range of data and signals. You will learn topics such as sampling, signal filtering, noise reduction, data compression, the discrete Fourier transform (and fast Fourier transform), Fourier analysis, and feature extraction. Modeling a random signal as a stochastic process is used to investigate the analysis and processing of signals from a statistical viewpoint.

7:21 - Natural Language Processing
Building on the knowledge gained through the Signal Processing and Machine Learning courses*, here you will 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 will also gain a deeper understanding of the principles governing the development of generative AI models.

11:42 - Embedded Systems
You will learn about the different types of hardware platforms, software tools, and techniques used in the design of embedded systems. Focusing particularly on the application of microcontrollers, you will learn how to design, program, test, and debug embedded systems. You will develop hardware-level device drivers for connected sensors, implement real-time data processing, and work with communications interfaces.

15:05 - FPGA
In this course, you will learn how to use FPGA architecture and algorithms for deep neural network learning. You will 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.

17:20 - Machine Vision
In this course, you will 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 content. You will trace the development of machine vision capabilities, from traditional machine vision tools through to the latest neural network algorithm functionality.

19:36 - Distributed Computing
In this class, you will 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.

24:12 - Capstone
In this course that comes last in the curriculum, you will 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.

26:57 - Deep Learning
In this course, you will focus on the challenges and methods involved in processing sensor data as it streams, as opposed to static datasets. You will 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.

Learn more about the program: https://bit.ly/48adjWr