The schedule is tentative and subject to change (e.g., snow days).
Lecture  Topic  Reading 

Lecture 1 (9/4/18)  Course overview and introduction. Linear classification and the Perceptron algorithm  Slides 
Lecture 2 (9/6/18)  Review of concepts from linear algebra  Slides 
Lecture 3 (9/11/18)  Review of concepts from multivariate calculus 
Slides 
Lecture 4 (9/13/18)  Convex functions and sets 
Slides 
Lectures 5, 6 (9/18/18, 9/20/18)  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

Slides 
Lectures 7, 8 (9/25/18, 9/27/18)  Optimality conditions for general and convex problems 
Slides 
Lectures 9, 10 (10/2/18, 10/4/18)  Oracle models, iterative methods, and gradient descent 
Slides 
Lecture 11 (10/9/18)  No Class (Monday schedule) 

Lecture 12 (10/11/18)  Gradient descent for smooth and strongly convex functions 
Slides 
Lectures 13, 14 (10/16/10, 10/18/18,)  Prediction using expert advice: majority algorithms, multiplicative weights update algorithm 
Slides 
Lecture 15 (10/23/18)  Applications of multiplicative weights update framework, online optimization and learning 
Slides 
Lecture 16 (10/25/18)  Midterm review 
Slides 
Lecture 17 (10/30/18)  Inclass midterm exam 

Lecture 18 (11/1/18)  Introduction to linear programming, modeling using LPs 
Slides 
Lecture 19 (11/6/18)  LP duality 
Slides 
Lectures 20, 21, 22 (11/8/18, 11/13/18, 11/15/18)  Applications of duality: maxflowmincut theorem, minimax theorem in game theory, learning and boosting 
Slides 
Lecture 23 (11/20/18)  Introduction to discrete optimization 
Slides 
Lecture 24 (11/27/18)  Submodular functions and optimization 
Slides 
Lecture 25 (11/29/18)  Maximum flows and minimum cuts in networks (guest lecture by Adrian Vladu) 

Lecture 26 (12/4/18)  Submodular optimization continued 
Slides 
Lecture 27 (12/6/18)  Final exam review 
Slides 
Lecture 28 (12/11/18)  Course recap 
Homeworks are released on Thursdays before class, and are due in one week on Thursdays at midnight. The hw schedule is as follows (see Piazza for the pdf/tex):