The course is divided into four major units, each culminating with an applied assignment and a quiz.
- BB: Deep Learning Foundations and Concepts by Christopher M. Bishop with Hugh Bishop. [BB online access link].
- GBC: Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron C. [GBC online access link]
- JM: Speech and Language Processing (3rd edition) by Dan Jurafsky and James H. Martin. [JM online access link].
- SB: Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto [SB online access link].
- RLM: Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka, Yuxi (Hayden) Liu, and Vahid M. Full book not available for free; can view [freely view RLM code examples here] or [optionally purchase RLM full text here].
Please note that the following schedule is tentative and subject to change.
| Week | Date | Class | Topic | References | Assignments |
|---|---|---|---|---|---|
| Unit 1: Introduction to Machine Learning | |||||
| 1 | W 1/8 | L01 | What is Machine Learning? | GBC 1; BB 1.1-1.2 | |
| F 1/10 | |||||
| 2 | M 1/13 | L02 | Python, NumPy, Jupyter | Jupyter Interface; Python Tutorial; NumPy Quickstart | |
| W 1/15 | L03 | Linear Regression and Scikit-Learn | GBC 5.1-5.2; RLM 3-4; Scikit-Learn Getting Started | Assignment 1 release | |
| F 1/17 | |||||
| 3 | M 1/20 | NO MEETING | MLK Jr. Holiday | ||
| W 1/22 | L04 | Logistic Regression | GBC 5.2-5.3; BB 5.2.5-5.2.6; RLM 6 (optional: GBC 5.4-5.5) | ||
| F 1/24 | |||||
| 4 | M 1/27 | L05 | Regularization and Validation | GBC 5.2-5.3; RLM 3, 6; BB 9 | |
| W 1/29 | L06 | Other models and unit 1 wrapup | |||
| F 1/31 | |||||
| Unit 2: Artificial Neural Networks, Convolutions and Image Recognition | |||||
| 5 | M 2/3 | L07 | Artificial Neural Networks | GBC 6-6.4; BB 6; RLM 12; PyTorch tutorial | |
| W 2/5 | L08 | Backpropagation in Neural Networks | GBC 6.5; BB 8; RLM 13; PyTorch tutorial | ||
| F 2/7 | Assignment 1 due 5pm | ||||
| 6 | M 2/10 | L09 | Minibatch Stochastic Gradient Descent | GBC 7.8, 7.12, 8-8.5; BB 7; RLM 13; PyTorch tutorial | Assignment 2 release |
| W 2/12 | Q1 | Quiz 1 | |||
| F 2/14 | |||||
| 7 | M 2/17 | L10 | Introducing Convolutional Neural Networks | GBC 9-9.5; BB 10.1-10.2; RLM 14 | |
| W 2/19 | L11 | Applying Convolutional Neural Networks in Vision | BB 10.3-10.5; RLM 14 | ||
| F 2/21 | |||||
| Unit 3: Transformers and Language Models | |||||
| 8 | M 2/24 | L12 | Recurrenct Neural Networks and Attention | JM 8, GBC 10, RLM 15 | |
| W 2/26 | L13 | Attention and the Transformer Architecture | BB 12.1; RLM 16; JM 9.1-9.3 | ||
| F 2/28 | Assignment 2 due 5pm | ||||
| 9 | M 3/3 | L14 | Transformer Large Language Models: Part 1 | JM 9.4-9.5, 10-11; BB 12.2-12.3; RLM 16 | Assignment 3 release |
| W 3/5 | Q2 | Quiz 2; Quiz 1 Redux | |||
| F 3/7 | |||||
| 10 | M 3/10 | NO MEETING | Spring recess | ||
| W 3/12 | NO MEETING | Spring recess | |||
| F 3/14 | NO MEETING | Spring recess | |||
| 11 | M 3/17 | L15 | Transformer Large Language Models: Part 2 | JM 9.4-9.5, 10-11; BB 12.2-12.3; RLM 16 | |
| W 3/19 | L16 | Language Model Alignment and Prompting | JM 12 | ||
| F 3/21 | |||||
| Unit 4: Reinforcement Learning and Behavior | |||||
| 12 | M 3/24 | L17 | Introduction to Reinforcement Learning | SB 3-4; RLM 19 | |
| W 3/26 | L18 | Model Free Q-Learning | SB 6; RLM 19 | ||
| F 3/28 | Assignment 3 due 5pm | ||||
| 13 | M 3/31 | L19 | Deep Q-Learning | SB 9-10; RLM 19 | Assignment 4 release |
| W 4/2 | Q3 | Quiz 3; Quiz 2 Redux | |||
| F 4/4 | |||||
| 14 | M 4/7 | L20 | Implementing (Deep) Q-Learning | SB 6, 9-10; RLM 19 | |
| W 4/9 | L21 | Policy-Based Reinforcement Learning | SB 13 | ||
| F 4/11 | |||||
| Bonus Reel, Review, and Wrapup | |||||
| 15 | M 4/14 | L22 | Bonus Reel 1: Generative Adversarial Networks | BB 17 | Assignment 4 due 5pm |
| W 4/16 | L23 | Quiz 4; Quiz 3 Redux | |||
| F 4/18 | |||||
| 16 | M 4/21 | Q4 | Bonus Reel 2: Diffusion Models | BB 20 | |
| W 4/23 | L24 | Wrapup; Quiz 4 Redux | |||
