The course is divided into four major units, each culminating with an applied assignment and a quiz. In the last portion of the course, we will read recent research in machine learning. There is also a comprehensive final exam. References are to the following.
- 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].
Specific chapter references will be added to the schedule under the references column. Please note that the following schedule is tentative and subject to change.
| Week | Date | Class | Topic | References | Assignments |
|---|---|---|---|---|---|
| Unit 1: Introduction to Python and Machine Learning | |||||
| 1 | M 8/26 | 1 | What is Machine Learning? | GBC 1; BB 1.1-1.2 | |
| W 8/28 | 2 | Python, NumPy, Jupyter | Jupyter Interface; Python Tutorial; NumPy Quickstart | ||
| 2 | M 9/2 | No meeting, Labor Day Holiday | |||
| W 9/4 | 3 | Linear Regression and Scikit-Learn | GBC 5.1-5.2; RLM 3-4; Scikit-Learn Getting Started | Assignment 1 release | |
| 3 | M 9/9 | 4 | Logistic Regression | GBC 5.2-5.3; BB 5.2.5-5.2.6; RLM 6 (optional: GBC 5.4-5.5) | |
| W 9/11 | 5 | Logistic Regression continued | |||
| 4 | M 9/16 | 6 | Regularization and Validation, Other Models | GBC 5.2-5.3; RLM 3, 6; BB 9 | |
| Unit 2: Artificial Neural Networks, Convolutions and Image Recognition | |||||
| W 9/18 | 7 | Artificial Neural Networks | GBC 6-6.4; BB 6; RLM 12 | ||
| F 9/20 | Assignment 1 due 5 pm | ||||
| 5 | M 9/23 | 8 | Training Neural Networks | GBC 6.5, 7.8, 7.12, 8-8.5; BB 7-8; RLM 13 | |
| W 9/25 | 9 | Quiz 1 | Assignment 2 release | ||
| 6 | M 9/30 | 10 | Convolutional Neural Networks and Applications | GBC 9-9.5; BB 10-10.2; RLM 14 | |
| W 10/2 | 11 | Object Detection & Image Generation in Brief | BB 10.3-10.5, 17, 20; RLM 17 | ||
| Unit 3: Transformers and Language Models | |||||
| 7 | M 10/7 | 12 | Self-Attention Mechanisms and Transformer Architectures | ||
| W 10/9 | 13 | Training Language Models | |||
| F 10/11 | Assignment 2 due 5 pm | ||||
| 8 | M 10/14 | No meeting, Fall Break | |||
| W 10/16 | 14 | Language Models and In-Context Learning | Assignment 3 release | ||
| 9 | M 10/21 | 15 | Quiz 2 | ||
| W 10/23 | 16 | Language Model Applications and Wrapup | |||
| Unit 4: Reinforcement Learning | |||||
| 10 | M 10/28 | 17 | Introduction to Reinforcement Learning | ||
| W 10/30 | 18 | Model-Based Reinforcement Learning Algorithms | |||
| F 11/1 | Assignment 3 due 5 pm | ||||
| 11 | M 11/4 | 19 | Model-Free Q-Learning | ||
| W 11/6 | 20 | Quiz 3 | Assignment 4 release | ||
| 12 | M 11/11 | 21 | Value-Based Deep Reinforcement Learning | ||
| W 11/13 | 22 | Policy-Based Deep Reinforcement Learning | |||
| Research in Machine Learning | |||||
| 13 | M 11/18 | 23 | Research in Machine Learning | ||
| W 11/20 | 24 | Research Paper Presentations 1 | |||
| F 11/22 | Assignment 4 due | ||||
| 14 | M 11/25 | 25 | Quiz 4 | ||
| W 11/27 | No meeting, Thanksgiving break | ||||
| 15 | M 12/2 | 26 | Research Paper Presentations 2 | ||
| W 12/4 | 27 | Research Paper Presentations 3 | |||
| End of Classes | |||||
| 16 | M 12/9 | No meeting, reading period | |||
| W 12/11 | Comprehensive Final Exam: 9 am - noon | ||||
