There are several recommended books for this course:
Programming experience is strongly recommended for this course. Please work through the following tutorial if you do not have programming experience:
| Week of | Topics | Recommended readings covering this week's lecture | Assignments due this week (posted on Canvas) |
|---|---|---|---|
| Jan 25 | Course Introduction (lecture slides) |
Chapter 1 of Geron book; pg. 95-103 of Goodfellow book | |
| Feb 1 | Regression, Regularization (lecture slides) |
pg. 33-52, 66-67, 107-112, 123-134 of Geron book; pg. 104-118 of Goodfellow book; (Optional Linear Algebra Background) Chapter 2 of Goodfellow book | Problem Set 1 |
| Feb 8 | Artificial Neurons, Gradient Descent (lecture slides) |
Chapter 3, pg. 113-123, and pg. 255-262 of Geron book; pg. 10-34 of Rashid book | Problem Set 2 |
| Feb 15 | Cancelled due to weather | ||
| Feb 22 | Cancelled due to weather | ||
| Mar 1 | Nearest Neighbor, Decision Tree (lecture slides) |
Nearest Neighbor; Chapter 6 of Geron book | Lab Assignment 1 |
| Mar 8 | Naive Bayes, Support Vector Machine (lecture slides) |
Chapter 5 of Geron book; Chapter 5.6 in Goodfellow book | Problem Set 3 |
| Mar 15 | No Class (Spring Break) | ||
| Mar 22 | Ensemble Learning (lecture slides) |
Chapter 7 of Geron book | Problem Set 4 |
| Mar 29 | Feature Representation, Dimensionality Reduction (lecture slides) |
Chapter 8 of Geron book; Chapters 12.2 and 12.4 of Goodfellow book | Lab Assignment 2 |
| Apr 5 | Neural Networks (lecture slides) |
pg. 263-265 of Geron book; Chapter 11 of Geron book (pgs. 277-306); pg. 35-71 of Rashid book, pgs. 163-171 and pgs. 185-217 of Goodfellow book | Problem Set 5; Project Pre-Proposal |
| Apr 12 | Convolutional Neural Networks (lecture slides) (Guest: Dr. Suyog Jain, PathAI) |
Chapter 13 of Geron book | Lab Assignment 3 |
| Apr 19 | Recurrent Neural Networks (lecture slides) (Guest: Dr. Cheryl Martin, Alegion) |
Chapter 14 of Geron book | Project Proposal |
| Apr 26 | Algorithm Fairness, Accountability, Transparency, and Ethics (lecture slides) (Guest: Dr. Mehrnoosh Sameki, Microsoft) |
Project Outline | |
| May 3 | Unsupervised Learning, Active Learning, Curriculum Learning, Reinforcement Learning (lecture slides) |
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| May 10 | No Class | Final Project with Project Videos |