References are to:
- BB: Deep Learning Foundations and Concepts by Christopher M. Bishop with Hugh B [BB online access link].
- JM: Speech and Language Processing (3rd edition) by Dan Jurafsky and James H. M [JM online access link]
- SB: Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto [SB online access link].
| Week | Date | Class Topics (class meets M/W) | References | Assignments Due (11:59 pm) |
| 1 | W 1/7 | 01: What is Machine Learning? Linear Models | BB 1 | |
| Th 1/8 | ||||
| F 1/9 | ||||
| 2 | M 1/12 | 02: Maximum Likelihood Estimation, Validation and Regularization | BB 4.1 | |
| Tu 1/13 | Recommended (nothing to turn in): Complete Setup and Background | |||
| W 1/14 | 03: Logistic Regression and Classification, Cross Entropy and Evaluation Metrics | BB 5-5.1.4, 5.2.5-5.2.6, and 5.4-5.4.4 | ||
| Th 1/15 | ||||
| F 1/16 | ||||
| 3 | M 1/19 | MLK Jr. Holiday | ||
| Tu 1/18 | HW 1 | |||
| W 1/21 | Finishing 03: Logistic Regression and Classification, Cross Entropy and Evaluation Metrics | |||
| Th 1/22 | ||||
| F 1/23 | ||||
| 4 | M 1/26 | 04: Artificial Neural Networks and the Multilayer Perceptron | BB 6.2-6.3.4, 6.3.6-6.4 | |
| Tu 1/27 | HW 2 | |||
| W 1/28 | 05: Backpropagation and Minibatch Stochastic Gradient Descent | BB 7.2-7.4, 8.1-8.1.3, 9.3, 9.6.1 | ||
| Th 1/29 | ||||
| F 1/30 | ||||
| 5 | M 2/2 | 06: Convolutional Neural Networks and Image Recognition | BB 9.5, 10-10.2 | |
| Tu 2/3 | (asynchronous supplement) PyTorch Introduction | HW 3 | ||
| W 2/4 | 07: Convolutional Neural Networks and Object Detection | BB 10.3-10.5 | ||
| Th 2/5 | ||||
| F 2/6 | ||||
| 6 | M 2/9 | 08: Sequence Models and the Transformer Architecture | BB 12-12.1, JM 8.1-8.7 | |
| Tu 2/10 | HW 4 | |||
| W 2/11 | 09: Transformer Language Models and Autoregressive Generation | BB 12.2-12.2.4, JM 7 | ||
| Th 2/12 | ||||
| F 2/13 | ||||
| 7 | M 2/16 | 10: Fine-tuning, Low Rank Adaptation, KV-Caching, and Quantization | BB 12.3.5, JM 8.8 & 9, 10.4 | |
| Tu 2/17 | HW 5 | |||
| W 2/18 | 11: Chain of Thought, In-Context Learning, and Retrieval-Augmented Generation | JM 7.3, 9.4, 11 | ||
| Th 2/19 | ||||
| F 2/20 | ||||
| 8 | M 2/23 | Exam 1 (required): Covers L01-09; HW 1-5 | ||
| Tu 2/24 | ||||
| W 2/25 | 12: Vision Transformers and Contrastive Learning | BB 6.3.5, 12.4 | ||
| Th 2/26 | ||||
| F 2/27 | ||||
| 9 | M 3/2 | 13: Generative Models and Diffusion | BB 20 | |
| Tu 3/3 | HW 6 | |||
| W 3/4 | 14: Text-to-Image and Guided Diffusion | BB 12.4 | ||
| Th 3/5 | ||||
| F 3/6 | ||||
| 10 | M 3/9 | Spring Recess | ||
| Tu 3/10 | Spring Recess | |||
| W 3/11 | Spring Recess | |||
| Th 3/12 | Spring Recess | |||
| F 3/13 | Spring Recess | |||
| 11 | M 3/16 | 15: Markov Decision Processes | SB 3 | |
| Tu 3/17 | ||||
| W 3/18 | Exam 1 Retake (optional) | |||
| Th 3/19 | ||||
| F 3/20 | ||||
| 12 | M 3/23 | 16: Value-Based Reinforcement Learning | SB 4.4, 5.2-5.3, 6.5 | |
| Tu 3/24 | HW 7 | |||
| W 3/25 | 17: Policy-Based Reinforcement Learning | SB 13 | ||
| Th 3/26 | ||||
| F 3/27 | ||||
| 13 | M 3/30 | 18: State of the Art Models, Applications, and Projects | ||
| Tu 3/31 | HW 8 | |||
| W 4/1 | Project Workshop 1 | |||
| Th 4/2 | ||||
| F 4/3 | ||||
| 14 | M 4/6 | Exam 2 (required): Covers L08-17; HW 5-8 | ||
| Tu 4/7 | ||||
| W 4/8 | Project Workshop 2 | |||
| Th 4/9 | ||||
| F 4/10 | ||||
| 15 | M 4/13 | Project Workshop 3 | ||
| Tu 4/14 | ||||
| W 4/15 | Project Workshop 4 | |||
| Th 4/16 | ||||
| F 4/17 | ||||
| 16 | M 4/20 | Exam 2 Retake (optional) | ||
| Tu 4/21 | ||||
| W 4/22 | LDOC and Project Workshop 5 | |||
| Th 4/23 | Reading Period | |||
| F 4/24 | Reading Period | |||
| 17 | Su 4/26 | Reading Period | Final Project | |
