ECE 8550 Artificial Intelligence
Revised on 12/20/2024
For the definitive and up-to-date course details, please refer to the syllabus on Canvas.
Course Overview
ECE 8550 Artificial Intelligence is an introductory course designed to immerse beginners into the world of deep learning using PyTorch. It provides hands-on experience in developing various types of deep neural networks and addresses contemporary issues in AI.
Course Description
Participants will gain practical skills in implementing neural network architectures, including fully connected, convolutional, and recurrent neural networks. The course covers key aspects of PyTorch programming and delves into practices for training and tuning deep neural networks. Additionally, it explores ethical and technical considerations in AI, such as security, privacy, fairness, and explainability.
Topics Covered
- Deep neural networks
- Fully connected neural networks
- Convolutional neural networks
- Recurrent neural networks
- PyTorch programming
- Training and tuning deep neural networks
- Deep learning frontiers
- Trustworthy AI (e.g., security, privacy, fairness, explainability)
Prerequisites
- ECE 4420/6420
- College Calculus and Linear Algebra
- Basic Probability and Statistics
- Machine Learning basics
- Python programming skills (Numpy, Pandas)
- Python for Beginners
- Google's Python Introduction
- NumPy Tutorial
- Pandas Tutorial
Course Materials
No textbooks are required for this course. Recommended for after-class reading:
- Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola. (2020). Dive into Deep Learning. Available Online
Grading Criteria
- Homework
- Paper reading
- Final project
- Attendance
Late Policy
- On the due date, the cutoff for on-time submission is 11:59 pm (Eastern Time).
- Late work is discounted 5% per calendar day late.
- Late submissions are not accepted after seven calendar days past the original due date and are graded as zero.