Machine Learning for Embedded Systems (Fall 2026)

ECE 554

News:

  • 08/25/2026 The course websites at instructor’s [Web] and canvas [Home] are active now.
  • 08/25/2026 Please complete [Poll 1] in the class.

Schedule and Documents

[Syllabi]

W Date Topic Documents Reading Material
1 Aug 27 Course Information & Introduction to ML [Handson1] [Lab1]
2 Sep 03 MLP Programming with Pytorch
3 Sep 10 Train Neural Networks
4 Sep 17 Deep CNN - Part 1
5 Sep 24 Deep CNN - Part 2
6 Oct 01 Deep CNN - Part 3 + Mid-term review
7 Oct 08 Mid-Term Exam
8 Oct 15 Recurrent Neural Networks & Transformer
9 Oct 22 Model Compression
10 Oct 29 ML System Optimization
11 Nov 05 Final Project Progress Review
12 Nov 12 Neural Architecture Search
13 Nov 19 Hardware-Aware NAS
14 Dec 03 Final Project

Coure Inforamtion

Instructor Dr. Weiwen Jiang
E-Mail wjiang8@gmu.edu
Lecture Time Thursday 16:30 pm – 19:10 pm
In-person Session Location Room 1113, Peterson Hall
Office Hour Thursday 14:00 - 15:00
Place Room 3247, Nguyen Engineering Building
TA Lingjun Xiong
E-Mail lxiong2@gmu.edu
Office Hour Monday, Thursday: 9:00-10:00

Course Materials

Course materials will be posted before or after the class. No formal textbook is required. This e-book will be referred to on the course.

Course Description

Machine learning (ML) has gradually become the core component of wide applications in different computing scenarios, ranging from edge computing to cloud computing. This course focuses on resource-constrained edge computing, in particular the embedded systems, and introduces techniques for developing energy/time efficient ML algorithms and models for the embedded systems. Topics that are covered include (i) commonly used ML algorithms, (ii) ML model compression techniques, (iii) hardware-aware machine learning, (iv) hardware and neural architecture co-design. The course also provides a comprehensive team-based research and development experience through projects and presentations. Offered by Electrical & Comp. Engineering. May not be repeated for credit.

Prerequisites

The course topics are self-contained so that a background in machine learning is not required. Students should be familiar with programming and embedded systems to complete the course projects.

Tools for Lab