Introduction to Deep Learning

Dept. of Electrical Engineering
University of Notre Dame


Spring 2025- EE 60572-1
Location/Time: DBRT 303 - TTh 2pm-3:15pm

Course Vault , Syllabus
Description: Deep learning technologies have matured to the point where all system engineers must be aware of their capabilities and limitations. Deep learning has demonstrated spectacular success in computer vision, timeseries forecasting, text/language analysis, and autonomous robots. This course uses the TensorFlow/Keras development framework to provide a hands-on introduction to deep learning. The course topics include
  1. Introduction - learning by example
  2. Generalization - a statistical approach
  3. Neural Network Models
  4. Deep Learning Training Pipelines
  5. Convolutional Neural Networks (Vision)
  6. Recurrent Neural Networks and Transformers (NLP)
  7. Generative Models
  8. Security and Fairness in Deep Learning
  9. Deep Reinforcement Learning
Prerequisites: Python Programming Experience! Familiarity with probability and linear algebra.
Grading: 40% 8 homework assignments, 30% (2) Midterms (in-class), 30% Final Exam (take home) or Project (optional)
Textbooks:
  • Dept. of EE, Univ. of Notre Dame, Introduction to Deep Learning, class lecture notes, Spring 2025
  • (RECOMMENDED) Francois Chollet, Deep learning with Python, 2nd edition, Manning, 2021.
  • (RECOMMENDED) Ian Goodfellow, Yoshua Bengio, Aaron, Courville, Deep Learning MIT Press, 2016.
Instructor: Michael Lemmon, Dept. of Electrical Engineering, University of Notre Dame, Fitzpatrick 266, (lemmon at nd.edu)