Instructor: ChoJui Hsieh Office location: Mathematical Sciences Building (MSB) 4232 Email: chohsieh@ucdavis.edu Office hours: 1pm2pm Wednesday 
I. Supvervised learning: regression and classification
II. Optimization for Largescale Machine Learning
 Linear regression
 Support Vector Machines
 Logistic regression
 Kernel methods
 Multiclass/Multilabel
III. Other Popular ML Algorithms
 Gradient descent
 Stochastic gradient descent
 Coordinate descent
 Duality
 Parallel Optimization Algorithms
 Matrix Completion
 Kernel Methods
 Treebased Algorithms (random forest, GBDT)
 Semisupervised Learning
 Ranking
 Neural Networks
Date 
Topic 
Readings and links 
Lectures 
Thurs 9/22

Course intro 

lecture1 
Tues 9/27 
Linear regression

lecture2


Thurs 9/29 
Intro to Optimization

lecture3  
Tues 10/4 
Parallel Optimization

lecture4  
Thurs 10/6 
Support Vector Machines and Linear ERM Models

lecture5  
Tues 10/11 
Matrix Completion (recommender systems): Scalable Algorithms

lecture6  
Thurs 10/13 
Matrix Completion (recommender systems)

lecture7 

Tues 10/18 
Multiclass and multilabel classification

lecture8 

Thurs 10/20 
Kernel Methods

lecture9  
Tues 10/25 
Nearest Neighbor and Maximum Inner Product Search

lecture10 

Thurs 10/27 
Treebased Learning Algorithms

lecture11


Tues 11/1 
Neural Network

lecture12


Thurs 11/3 
Individual discussions


Tues 11/8 
Paper presentations


Thurs 11/10 
Paper presentations


Tues 11/15 
Paper presentations


Thurs 11/17 
Paper presentations


Tues 11/22 
Paper presentations


Thurs 11/24 
Thanksgiving (no class)


Tues 11/29 
Final Project Presentations


Tues 12/1 
Final Project Presentations
