## 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.

• Math 33A Linear Algebra and Its Applications, Matrix Analysis
• Stat 100B Intro to Mathematical Statistics,
• CS 180 Intro to Algorithms and Complexity.
• Programming skills in Matlab or python.

### Textbook

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

• R. Duda, et al. Pattern Classification, John Wiley & Sons, 2001. [Good for CS students]
• T. Hastie, et al. The Elements of Statistical Learning, Spinger,2009. [Good for Stat students]
• C. Bishop, Pattern Recognition and Machine Learning, Springer, 2006 [with advanced materials]

### Instructors

• Prof. Song-Chun Zhu, sczhu@stat.ucla.edu, office: Boelter Hall 9404 Office Hours: Tuesday 1:00-3:00pm
• Reader: Yang Lu, yanglv@ucla.edu, office: Boelter Hall 9406. Office hours: Thursday 1:00-3:00pm

 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` ` `