Stat 231--- CS 276A

Pattern Recognition and Machine Learning

MW 9:30-10:50 Am, Fall 2011,     Math Science 5128 

www.stat.ucla.edu/~sczhu/Courses/UCLA/Stat_231/Stat_231.html

Course Description

This course introduces fundamental concepts, theories, and algorithms for pattern recognition and machine learning,
which are used in computer vision, speech recognition, data mining, statistics, information retrieval, and bioinformatics.
Topics include: Bayesian decision theory, parametric and non-parametric learning, data clustering, component analysis,
boosting techniques, kernel methods and support vector machine, and fast nearest neighbor indexing and hashing.

Prerequisites

Textbook

Instructors

Grading Plan: 4 units, letter grades

Two Homework assignments
20%

Three projects:

 

15%

15%

15%

No Middle-Term Exam
0%
Final Exam
35%

Grading policy

Tentative Schedule for 2011

Lecture
Date
Topics
Reading Materials
Handouts
1
09-26
Introduction to Pattern Recognition
[Problems, applications, examples, and project introduction]
Ch 1
2
09-28
Bayesian Decision Theory I
[Bayes rule, discriminant functions]
Ch 2.1-2.6
3
10-03
Bayesian Decision Theory II 
[loss functions and Bayesian error analysis]
Ch 2.1-2.6
4
10-05
Component Analysis and Dimension Reduction I:
[principal component analysis (PCA)], face modeling]
[Explanation of Project 1: code and data format]
Ch 3.8.1, 
Ch 10.13.1
Project 1 
5
10-10
Component  Analysis and Dimension Reduction II:
[Fisher Linear Discriminant ]
[ Multi-dimensional scaling (MDS)]
Ch 3.8.2, 
Ch10.14
6
10-12
Component  Analysis and Dimension Reduction III:
[Local Linear Embedding (LLE), Intrinsic dimension ]
paper
7
10-17
Boosting Techniques I:
[ Adaboost ]
Ch 9.5 
8
10-19
Boosting Techniques II:
[RealBoost and Example on face detection ]
[ Explanation of project II ]
9
10-24
Boosting Techniques III:
[Probabilistic analysis, Logit boost]
 
10
10-26
Non-metric method I:
 [ tree structured Classification: principle and example ]
Ch 8.1-8.3 
11
10-31
Non-metric method II:
Syntactic pattern recognition
Ch 8.5-8.8
12
11-02
Support vector machine I: 
 Kernel-induced feature space
Lect12-15.pdf

(xerox handout)
13
11-07
Support vector machine II: 
[ Support vector classifier ]
[ Explanation of project III ]
Ch 5.11 
14
11-09
Support vector machine III:
[Loss functions, Latent SVM]
 
 
15
11-14
Parametric Learning
       [ Maximum Likelihood Estimation (MLE) ]
        [ Sufficient Statistics and Maximum entropy ]
Ch 3.1-3.6
16
11-16
Non-parametric Learning I
[ Parzen window ]
Ch 4.1-4.5
17
11-21
Non-parametric Learning II:
[K-nn classifer and Error analysis]
Ch 4.6
handout
18
11-23
Non-parametric Learning III:
[K-nn fast approximate computing:  KD-tree and Hashing ]
19
11-28
 Data Clustering I:   
[K-mean clustering,  EM]
Ch 10.1-10.4
20
11-30
Data Clustering II: 
[EM,  mean-shift]