| Date | Event | Topics | Readings | Materials |
|---|---|---|---|---|
| Week 1 | ||||
| Jan 20, 2025 | Martin Luther King Jr. Day | |||
| Jan 22, 2025 | Lecture 1 | Course introduction, goals of neural networks | Book 1: 1.2.1-1.2.2, 7.1.1-7.2.4, 7.8.1, 7.8.2 | Prerequisites Notes slides |
| Week 2 | ||||
| Jan 27, 2025 | Lecture 2 | Linear regression, gradient descent | Book 1: 4.2.1, 4.2.2, 11.1-11.2.4 | Notes Slides Blank |
| Jan 29, 2025 | Lecture 3 | Linear regression, maximum likelihood | Book 1: 4.2.1, 4.2.2, 11.1-11.2.4 | Notes Slides |
| Jan 30, 2025 | Homework 1 Due | Assignment | ||
| Week 3 | ||||
| Feb 3, 2025 | Lecture 4 | Logistic regression | Book 1: 10.1, 10.2.1, 10.2.3, 10.3.1-10.3.3 | Notes Slides Blank |
| Feb 5, 2025 | Lecture 5 | Multinomial logistic regression | Book 1: 10.2.2 | Notes Slides |
| Feb 6, 2025 | Homework 2 Due | Assignment | ||
| Week 4 | ||||
| Feb 10, 2025 | Lecture 6 | Feature transforms | Book 1: 13.1, 13.2 | Notes Slides Blank |
| Feb 12, 2025 | lecture 7 | Neural networks | Book 1: 13.1, 13.2 | Notes |
| Feb 13, 2025 | Homework 3 Due | Assignment | ||
| Week 5 | ||||
| Feb 17, 2025 | Lecture 8 | Deep neural networks | Book 1: 13.1, 13.2 | Notes |
| Feb 19, 2025 | Lecture 9 | Automatic Differentiation | Book 1: 13.3 | Notes Slides |
| Feb 20, 2025 | Homework 4 Due | Assignment | ||
| Week 6 | ||||
| Feb 24, 2025 | Lecture 10 | Reverse-mode Automatic Differentiation | Book 1: 13.4.5, 13.5 | Notes |
| Feb 26, 2025 | Lecture 11 | PyTorch, computational costs of neural networks | Book 1: 13.5 | Notebook Notes Slides |
| Feb 27, 2025 | Homework 5 Due | Assignment | ||
| Week 7 | ||||
| Mar 3, 2025 | Lecture 12 | Evaluation, L1 & L2 regularization | Book 1: 8.4 | Notes |
| Mar 5, 2025 | Lecture 13 | Regularization (Cont.), Dropout | Book 1: 13.4.1-13.4.2 | Notes |
| Mar 6, 2025 | Homework 6 Due | Assignment | ||
| Week 8 | ||||
| Mar 10, 2025 | Lecture 14 | Stochastic gradient descent | Book 1: 14.1-14.3 | |
| Mar 12, 2025 | Lecture 15 | Stochastic gradient descent | Book 1: 14.1-14.3 | Slides |
| Mar 13, 2025 | Homework 7 Due | Assignment | ||
| Week 9 | ||||
| Mar 17, 2025 | Spring break | |||
| Week 10 | ||||
| Mar 24, 2025 | Lecture 16 | Stochastic gradient descent | Book 1: 15.1, 15.2.1-15.2.3 | Slides |
| Mar 26, 2025 | Lecture 17 | Residual networks and Normalization | Book 1: 15.1, 15.2.1-15.2.3 | Slides |
| Mar 27, 2025 | Ethics Warm-up Due | Assignment | ||
| Week 11 | ||||
| Mar 31, 2025 | Lecture 18 | Residual networks and Normalization (cont.) | Book 1: 15.2.5-15.2.7 | Slides |
| Apr 2, 2025 | Lecture 19 | Convolutional Networks | Book 1: 15.2.5-15.2.7 | Slides |
| Apr 3, 2025 | Homework 8 Due | Assignment | ||
| Week 12 | ||||
| Apr 7, 2025 | Lecture 20 | Autoencoders & U-Nets | Book 1: 15.2.1-15.2.6 | |
| Apr 9, 2025 | Lecture 21 | Image models, who owns data? | ||
| Apr 10, 2025 | Project Proposal Due | Assignment | ||
| Week 13 | ||||
| Apr 14, 2025 | Lecture 22 | Language models, RNNs | Book 1: 15.2.1-15.2.6 | Slides |
| Apr 16, 2025 | Lecture 23 | Attention layers | Slides | |
| Apr 17, 2025 | Homework 9 Due | Assignment | ||
| Week 14 | ||||
| Apr 21, 2025 | Lecture 24 | Transformers | Slides | |
| Apr 23, 2025 | Lecture 25 | Large language models, effects of LLMs | Slides | |
| Apr 24, 2025 | Project check-in due | Assignment | ||
| Week 15 | ||||
| Apr 28, 2025 | Lecture 26 | Addressing bias in machine learning | ||
| Apr 29, 2025 | Lecture 27 | When should we use machine learning? | ||
| Apr 30, 2025 | Homework 10 Due | Assignment | ||
| Finals | ||||
| May 9, 2025 | Final project due | Assignment | ||
| May 12, 2025 | Final project feedback due | |||
Course Calendar
This calendar is subject to change depending on the pace of the class and student interest.