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Introduction to Deep Learning
Dept. of Electrical Engineering
University of Notre Dame
Spring 2025- Enroll in Course Number EE 60572-01
Location/Time DBRT 303 - TTh 2pm-3:15pm
Office Hours - Fitz 266 - TBA
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Description: This is a survey course on deep learning for first year engineering
graduate students. The course topics include 1) training pipelines for neural network models, 2) Convolutional Neural Networks
for Image Processing, 3) Recurrent and Transformer Models for Sequence Data, 4) Generative Models (Autoencoders, GANs, and Diffusion), and 5) Security/Fairness in Machine Learning.
The course will make extensive use of TensorFlow/Keras deep learning libraries.
Grading: Grades will be based on 8 Homework assignments (40%). There will be two midterms (30%)
and final exam or project (30%). Project will be final paper/notebook subject to instructor approval. Homeworks and Exams will be a combination
of essay questions, traditional problem solving, and programming exercises.
Prerequisites:Students should have prior course work in linear algebra and random processes. Students must have prior Python
programming experience. Students will use TensorFlow/Keras deep learning libraries and will be customizing class objects used to instantiate and train
deep neural network models.
Project Option: Students may elect to do a project with instructor's approval.
Projects will be individual student efforts (no teams). See instructor as soon as possible if you are interested in the project option.
Instructor approval will be contingent on a satisfactory project proposal (due before Spring Break).
The project grade will be used in place of the final exam grade. Project proposal and final report must be written using
ICML latex style files (zip) (docx)
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TOPICS
- Introduction - Learning by Example
- Generalization - a statistical approach
- Neural Networks Models
- Deep Neural Network Training Pipeline
- Convolutional Neural Networks
- Recurrent Models and Transformers
- Homework 6
- Midterm 2 (in-class (4/1)
- Homework 7
- Generative Models
- Security/Fairness in Machine Learning
- Deep Reinforcement Learning
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Textbooks:
- (Required) Dept. of EE, University of Notre Dame,
Introduction to Deep Learning, lecture notes 2025
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(Recommended) Francois Chollet, Deep learning with Python, 2nd edition, Manning, 2021.
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(Suggested) Ian Goodfellow, Yoshua Bengio, Aaron Courville,
Deep Learning, MIT Press, 2016
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(Suggested) Richard Sutton and Andrew Barto,
Reinforcement Learning: an introduction,
second edition, MIT Press, 2018
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(Suggested) David Foster,
Generative Deep Learning
, second edition, O'Reilly Media, 2023
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(Suggested) Solon Barocas, Moritz Hardt, Arvind Narayanan,
Fairness and Machine Learning: limitations and Opportunities, to be published by MIT Press in 2023.
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