Foundations of Machine Learning
This was a mini-course that I taught in the summer of 2021 at Sharif University of Technology, where we covered fundamental concepts of machine learning. This includes review material on probability and optimization, and then covers fundamental concepts in machine learning, including PAC learning and learning guarantees, techniques in supervised learning (such as SVM and neural networks), unsupervised learning (including clustering and dimensionality reduction), along with other relevant concepts such as online learning and reinforcement learning. The course was designed for those with a basic understanding of probability and optimization who have had minimal exposure to ML concepts, with the goal of familiarizing them with different areas of machine learning and preparing them for more advanced material in their coursework or research.
Lecture Slides
| # | Topic | Slides |
|---|---|---|
| 1 | Introduction | |
| 2 | Probability | |
| 3 | PAC Learning | |
| 4 | Learning Guarantees | |
| 5 | Optimization | |
| 6 | SVM | |
| 7 | Online Learning | |
| 8 | Clustering | |
| 9 | Dimensionality Reduction | |
| 10 | Neural Networks | |
| 11 | Reinforcement Learning |