Machine Learning for Embedded Systems (Fall 2025)

ECE 554

News:

  • 08/28/2025 The course websites at instructor’s [Web] and canvas [Home] are active now.
  • 08/28/2025 [Lab 1] has been released on canvas.
  • 08/28/2025 Please complete [Poll 1] in the class.

Schedule and Documents

[Syllabi]

W Date Topic Documents Reading Material
1 Aug 28 Course Information & Introduction to ML [Handson1]
2 Sep 04 MLP Programming with Pytorch [Lab1] [Pytorch],[Tensor],[Model],[Train]
3 Sep 11 Train Neural Networks
4 Sep 18 Deep CNN - Part 1
5 Sep 25 Deep CNN - Part 2
6 Oct 02 Transformer
7 Oct 09 Model Compression - Part 1
8 Oct 16 Model Compression - Part 2
9 Oct 23 Mid-Term Exam
10 Oct 30 ML System Optimization
11 Nov 06 Reinforcement Learning
12 Nov 13 Neural Architecture Search
13 Nov 20 Hardware-Aware NAS
14 Dec 04 Co-Design & 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 2413, Peterson Hall
Office Hour Thursday 14:00 - 15:00
Place Room 3247, Nguyen Engineering Building
TA Hanhan Wu
E-Mail hwu28@gmu.edu

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