The schedule is tentative and subject to change (e.g., snow days).
You can try the Jupyter notebooks in your browser here. You can make changes and run the code in the browser. The changes will not be saved, when you hit refresh all the changes will be gone.
| Lecture | Topic | Annotated Slides | Other | |
|---|---|---|---|---|
| Lecture 1 (1/22/20) | Course overview and introduction. Linear classification and the Perceptron algorithm | Lecture 1 | ||
| Lecture 2 (1/27/20) | Review of concepts from linear algebra | Lecture 2 | Jupyter notebook | |
| Lecture 3 (1/29/20) | Review of concepts from multivariate calculus |
Lecture 3 | ||
| Lectures 4, 5 (2/3/20, 2/5/20) | Convex functions and sets |
Lectures 4,5 | ||
| Lectures 6, 7 (2/10/20, 2/12/20) | Introduction to optimization. Examples of discrete and continuous optimization problems: classification and learning problems (least squares, LASSO, SVM), maximum flows and minimum cuts, maximum cut, minimum independent set
|
Lectures 6,7 | Jupyter notebook | |
| Lectures 8, 9 (2/18/20, 2/19/20) | Optimality conditions for general and convex problems |
Lectures 8,9 | ||
| Lectures 10, 11 (2/24/20, 2/26/20) | Oracle models, iterative methods, and gradient descent |
Lectures 10,11 | ||
| Lecture 12 (3/2/20 ) | Gradient descent for smooth and strongly convex functions |
Lecture 12 | ||
| Lectures 13, 14 (3/4/20, 3/16/20) | Prediction using expert advice: majority algorithms, multiplicative weights update algorithm |
Lectures 13,14 | ||
| Lecture 15 (3/18/20) | Applications of multiplicative weights update framework |
Lecture 15 | ||
| Lecture 16 (3/23/20) | Midterm exam (take home exam, lecture cancelled) |
Lecture 17 (3/25/20) | Online optimization and learning, follow the leader algorithm for online convex optimization |
Lecture 17 |
| Lecture 18 (3/30/20) | Online convex optimization continued: follow the regularized leader, online gradient descent. Introduction to linear programming. |
Lecture 18 | ||
| Lecture 19 (4/1/20) | Modeling using LPs, LP duality |
Lecture 19 | ||
| Lecture 20 (4/6/20) | Applications of duality I: 2-player games, Nash equilibria, minimax theorem |
Lecture 20 | ||
| Lectures 21, 22 (4/8/20, 4/13/20) | Applications of duality II: minimax theorem, boosting theorem, maxflow-mincut theorem |
Lecture 21, 22 | ||
| Lectures 23, 24 (4/15/20, 4/22/20) | Maximum flows and minimum cuts in networks (guest lectures by Adrian Vladu) |
Lecture 23, Lecture 24 | ||
| Lecture 25 (4/27/20) | Course recap |
Lecture 25 | ||
| Lecture 26 (4/29/20) | Midterm exam (take-home exam, lecture cancelled) |
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Acknowledgments: Many pictures used in the lecture slides are courtesy of Google Images and their respective authors. I am indebted to my colleagues at other institutions for some of the material in the lectures. In particular, Amir Ali Ahmadi's course at Princeton has been a great source of inspiration and material. The specific references/credits are on the References slide at the end of each lecture.
Homeworks are released on Wednesday before class, and are due in two weeks on Wednesday at midnight. Expect a homework every two weeks except for the midterm exam week.