Introduction
This course systematically introduces the fundamental algorithms and theories of machine learning, with an emphasis on the mathematical principles, model derivation, and practical applications of machine learning methods. The course covers regression, classification, support vector machines, neural networks, generative models, and clustering.
Course Agenda:
| Date | Lectures | Topic | Handouts |
|---|---|---|---|
| Part I: Foundations of Machine Learning | |||
| Thu. Sept. 17 | Course Overview |
|
[Slide] |
| Thu. Sept. 17 | Introduction to ML |
|
[Slide] |
| Fri. Sept. 18 | Optimization for Machine Learning |
|
[Slide] |
| Fri. Sept. 24 | Model Selection and Evaluation |
|
[Slide] |
| Part II: Machine Learning Models | |||
| TBD | Linear Models & Logistic Regression |
|
[Slide] |
| TBD | Tree-Based Models & Ensemble Models |
|
[Slide] |
| TBD | Margin-Based Models |
|
[Slide] |
| Part III: Deep Learning | |||
| TBD | Neural Network Fundamentals |
|
[Slide] |
| TBD | Neural Network Architectures |
|
[Slide] |
| Part IV: Foundation Models | |||
| TBD | Representation Learning |
|
[Slide] |
| TBD | Vision-Language Models |
|
[Slide] |
| TBD | Large Language Models |
|
[Slide] |