ECS 289G Scalable Machine Learning Fall 2016
Fall 2016


Tues/Thurs 4:40 pm - 6:00 pm




Instructor: Cho-Jui Hsieh
Office location: Mathematical Sciences Building (MSB) 4232
Email: chohsieh@ucdavis.edu
Office hours: 1pm-2pm Wednesday




Announcements

Here's a Slack page to get to know each other and find project/presentation partners: slack page . Thanks Phuong!

Overview

Course description

This is a course in machine learning for big data. The emphasis will be on developing scalable/parallel algorithms for various machine learning tasks. In addition to lectures on background material by the instructor, the course will also have paper presentations by students. Topics covered are expected to be: regression, classification, clustering, dimensionality reduction, matrix completion, parallel programming, optimization, etc. A substantial portion of the course will focus on research projects, where students will choose a well defined research problem.


Syllabus

    A high-level summary of the syllabus is as follows:
I. Supvervised learning: regression and classification
II. Optimization for Large-scale Machine Learning
III. Other Popular ML Algorithms

Grading Policy

Grades will be determined as follows:


Schedule

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
Multi-class and multi-label classification


lecture8
Thurs 10/20
Kernel Methods

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


lecture10
Thurs 10/27
Tree-based 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