STAT424  Optimization for Statistical Learning
Spring 2022
Time: Tu/F 1:002:30pm in Microsoft Teams
Instructor

Donlapark Ponnoprat
Office: STB304
References
 Amir Ali Ahmadi, Computing and Optimization (ORF 363) Lecture Notes.
 Boyd and Vandenberghe, Convex Optimization (2014) book.
 David Childers, Forecasting for Economics and Business (73423) Lecture Notes.
 Julian McAuley, Personalized Machine Learning (2022) book draft.
Lecture Notes
Lecture  Topic  Slides 

1  Some games and motivation  Lecture 1 
2  Linear algebra & calculus review 1  Lecture 2 notebook 
3  Linear algebra & calculus review 2  Lecture 3 
4  Unconstrained optimization  Lecture 4 
5  First & second order optimization, convex sets  Lecture 5 
6  Convex functions  Lecture 6 
7  Convex optimization  Lecture 7 notebook 
8  Applications of convex optimization  Lecture 8 
9  Bisection, Newton, and secant methods  Lecture 9 
10  Gradient descent methods  Lecture 10 
11  Multivariate Newton and the GaussNewton algorithm  Lecture 11 
12  Conjugate direction methods  Lecture 12 
13  Linear programming  Lecture 13 
14  Geometry and simplex method  Lecture 14 
15  Duality  Lecture 15 
16  Applications of linear programs and duality  Lecture 16 
17  Linear programs in game theory  Lecture 17 
18  Constrained convex optimization, FrankWolfe algorithm  Lecture 18 
19  Regularization, Sparsity and energy minimization  Lecture 19 
20  Online convex optimization I  Lecture 20 
21  Online convex optimization II  Lecture 21 
22  Nonconvex optimization: neural networks and recommender systems  Lecture 22 