Stat 231--- CS 276A

Pattern Recognition and Machine Learning

MW 3:30-4:45 PM, Fall 2016, Kinsey Pavilion 1220B        

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

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 deep learning with neural networks.

Prerequisites

You don't have to take exactly these courses as long as you know the materials.

Textbook

Textbook is not mandetary if you can understand the lecture notes and handouts.

Instructors

Grading Plan: 4 units, letter grades

Four projects:

 

15%

15%

15%

15%

Final Exam: Dec 8, Thursday 3:00-6:00pm (close book exam)
40%

Grading policy

Tentative Schedule for 2016 (Once the enrollment is fixed, course materials will be posted on the CCLE site.)

Lecture
Date
Topics
Handouts
1
09-26
Introduction to Pattern Recognition
[Problems, applications, examples,and project 0 ]
2
09-28
Bayesian Decision Theory I
[Bayes rule, discriminant functions]
 
3
10-03
Bayesian Decision Theory II 
[loss functions and Bayesian error analysis]
 
4
10-05
Component Analysis and Dimension Reduction I:
[PCA, face modeling, Project 1: code and data format]
 
5
10-10
Component  Analysis and Dimension Reduction II:
[Fisher Linear Discriminant ]
[Multi-dimensional scaling (MDS)]
 
6
10-12
Component  Analysis and Dimension Reduction III:
[Local Linear Embedding (LLE), Intrinsic dimension]
 
7
10-17
Boosting Techniques I:
  [perceptron, backpropagation and Adaboost]
 
8
10-19
Boosting Techniques II:
[RealBoost and Example on face detection]
[ Explanation of project II ]
 
9
10-24
Boosting Techniques III:
[analysis, logit boost, cascade and decision policy]
 
10
10-26
Boosting Techniques III:
[analysis, logit boost, cascade and decision policy]
 
11
10-31
Non-metric method I:
 [Decision tree and random forrest]
 
12
11-02
Non-metric method II:
Syntactic pattern recognition 
and example on human parsing
 
13
11-07
Support vector machine I: 
 Kernel-induced feature space 
 
14
11-9
Support vector machine II: 
[Support vector classifier]
[Explanation of project III]
 
15
11-14
Support vector machine III:
[Loss functions, Latent SVM]
 
16
11-16
Parametric Learning
       [ Maximum Likelihood Estimation (MLE) ]
        [ Sufficient Statistics and Maximum entropy]
 
17
11-21
Non-parametric Learning I
[ Parzen window and K-nn classifer]
 
18
11-23
Non-parametric Learning II:
[K-nn classifer and Error analysis]
 
19
11-28
 Deep Learning I
20
11-30
 Deep Learning II