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CSE 6363 (Fall 2008)
Machine Learning

Heng Huang
[ Administrative Basics | Course Description | Assignments | Outline of Lectures ]

Administrative Basics

Lecture

GACB 105 | Mondays and Wednesdays 2:30-3:50 PM
Instructor

Heng Huang | Nedderman Hall 308 | Office hours: Mondays and Wednesdays 4:00-5:20 PM
Textbook

Required:
Pattern Recognition and Machine Learning, Christopher M. Bishop, 2006.

Optional:
Pattern Classification, 2nd edition, Richard Duda, Peter Hart, David Stork.
The Elements of Statistical Learning, T. Hastie, R. Tibshirani, J. H. Friedman.
Work

Three homework sets. (30%)
Class presentations. (20%)
Final project. (30%)
Participation. (20%)
Request

Good math and programming background, for Master student, CSE 4308 or CSE 5360 (Artificial Intelligence I) is requested.

Course Description

This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in learning and pattern recognition or who may need to apply learning or pattern recognition techniques to a target problem (e.g. computer vision, bioinformatics, data mining, speech recognition, etc.). The following broad categories will be covered:
1) Introduction to Pattern Recognition and Learning Systems
2) Regression
2) Bayesian Learning
3) Non-parametric methods
4) Linear Discriminants and Support Vector Machines
5) Neural Networks
6) Decision Trees
7) Feature selection
8) Model selection
9) Introduction to learning theory
10) Unsupervised learning methods

Assignments

Homeworks

Three homeworks will be assigned later.
Paper presentations

Each student is expected to present one paper in the course of the semester and lead the discussion on the paper. The paper will be distributed to students later.
Project

Students will be asked to prepare, submit and present one project which will due at the end of the semester (I will post a project list). Students can choose his/her own topic to investigate. You will need to submit a short (one page) proposal for the purpose of approval and feedback for the final project.

Outline of Lectures

Week 1.

Mon Aug 25: No Class, the lecturer will attend ACM KDD conference.
Wed Aug 27: Introduction to Machine Learning

Week 2.

Mon Sep 1: No Class, Labor Day
Wed Sep 3: Basic Machine Learning: Probability Theory, Curve Fitting, Decision Theory, Information Theory

Week 3.

Mon Sep 8: Probability Distributions
Wed Sep 10: Linear Models 1

Week 4.

Mon Sep 15: Linear Models II
Wed Sep 17: Linear Models for Classification I

Week 5.

Mon Sep 22: Linear Models for Classification II
Wed Sep 24: Neural Network I

Week 6.

Mon Sep 29: Neural Network II
Wed Oct 1: Linear Algebra Review and Convex Optimization Overview I

Week 7.

Mon Oct 6: Convex Optimization Overview II
Wed Oct 8: Learning with Kernels I

Week 8.

Mon Oct 13: Learning with Kernels II
Wed Oct 15: Learning with Kernels III

Week 9.

Mon Oct 20: PCA and SVD
Wed Oct 22: Probabilistic PCA and Kernel PCA

Week 10.

Mon Oct 27: Graphical Models I
Wed Oct 29: Graphical Models II

Week 11.

Mon Nov 3: EM Algorithm
Wed Nov 5: Sampling Methods I

Week 12.

Mon Nov 10: Sampling Methods II
Wed Nov 12: Hidden Markov Models

Week 13.

Mon Nov 17: Class Presentations
Wed Nov 19: Class Presentations

Week 14.

Mon Nov 24: Decision Tree
Wed Nov 26: Boosting

Week 15.

Mon Dec 1: Advanced Topics in Machine Learning I
Wed Dec 3: Advanced Topics in Machine Learning II