Pattern Recognition and Machine Learning

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Stat 231--- CS 276APattern Recognition and Machine Learning

MW 3:30-4:45 PM, Fall 2013,     Math Science 5147 www.stat.ucla.edu/~sczhu/Courses/UCLA/Stat_231/Stat_231.html

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Course DescriptionThis 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

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

Textbook

• R. Duda, P. Hart, D. Stork, "Pattern Classification", second edition, 2000. [Good for CS students]
• T. Hastie, R. Tibshurani, and J.H. Friedman, "The Elements of Statistical Learning: Data Mining, Inference, and Prediction", Spinger Series in Statistics, 2001. [Good for Statistics students]
  

Instructors

• Prof. Song-Chun Zhu, sczhu@stat.ucla.edu, 310-206-8693, office BH 9404.
  Office Hours: Monday 1:00-3:00pm
• Reader: Joyce Meng, Statistics Graduate Student Lounge, joycemeng@ucla.edu
  Office hours: Friday 9-11:00am
  

Grading Plan: 4 units, letter grades

Two Homework assignments [td=37] 20%

Three projects: 1. Face modeling by AAM Model how many bits do you need to represent a face? 2. Face detection by AdaBoost how many features do you need to detect a face? 3, Object classification by SVM and DPM classifying objects (pedstrains, vehicles) in video [We may explore alternative project on Q/A ranking for Jeopardy! ] [td] 15% 15% 15%

Middle-Term Exam: No. [td] 0%

Final Exam: Dec 10, Tuesday 11:30AM-2:30PM (we only need 2 hours 12:15-14:15, close book exam) [td] 35%

Grading policy

• Homework policy:
  Homework must be finished independently. Do not discuss with classmates.
• Project policy:
  You are encouraged to work and discuss in a group, but each person must finish his/her own project. Hand in
  (i) a brief description of the experiment in hard copy, (ii) results and plots in hard copy, (iii) your code in e-copy to the reader.
• Late policy:
  You have a total of three late days for the class, but after using the three late days, no credit will be given for late homework/project.
  

Tentative Schedule for 2013

Lecture	Date	Topics	Reading Materials	Handouts
1	09-30	Introduction to Pattern Recognition[Problems, applications, examples, and project introduction]	Ch 1	syllabus.pdfLect1.pdf
2	10-02	Bayesian Decision Theory I[Bayes rule, discriminant functions]	Ch 2.1-2.6	Lect2.pdf
3	10-07	Bayesian Decision Theory II [loss functions and Bayesian error analysis]	Ch 2.1-2.6	Lect3.pdf
4	10-09	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 10.13.1Project 1	HW1Lect4-5.pdf
5	10-14	Component  Analysis and Dimension Reduction II:[Fisher Linear Discriminant ][Multi-dimensional scaling (MDS)]	Ch 3.8.2, Ch10.14	FisherFace.pdfLect5-6.pdf
6	10-16	Component  Analysis and Dimension Reduction III:[Local Linear Embedding (LLE), Intrinsic dimension]	paper	LLE paper
7	10-21	Boosting Techniques I:  [perceptron, backpropagation and Adaboost]	Ch 9.5	Lect7-9.pdf
8	10-23	Boosting Techniques II:[RealBoost and Example on face detection][ Explanation of project II ]	Tutorial Handout 1Handout 2
9	10-28	Boosting Techniques III:[Probabilistic analysis, Logit boost]
10	10-30	Non-metric method I: [tree structured Classification: principle and example]	Ch 8.1-8.3	Lect10.pdf
11	11-04	Non-metric method II:Syntactic pattern recognition and example on human parsing	Ch 8.5-8.8	Lect11.pdf
12	11-06	Support vector machine I:  Kernel-induced feature space	Tutorial paper	Lect12-15.pdf
	11-11	Veterans day holiday
13	11-13	Support vector machine II: [Support vector classifier][Explanation of project III]	Ch 5.11
14	11-18	Support vector machine III:[Loss functions, Latent SVM, Neual networks and DeepNet]
15	11-20	Parametric Learning       [ Maximum Likelihood Estimation (MLE) ]        [ Sufficient Statistics and Maximum entropy ]	Ch 3.1-3.6	Lect16.pdf
16	11-25	Non-parametric Learning I   [ Parzen window and K-nn classifer]	Ch 4.1-4.5	Lect17.pdf
17	11-27	Non-parametric Learning II:[K-nn classifer and Error analysis]	Ch 4.6handout	Lect18.pdf
18	12-02	Non-parametric Learning III: [K-nn fast approximate computing:KD-tree and Hashing ]	paper1paper2	Lect 19.pdf
19	12-04	Data Clustering and Bi-clustering:   [K-mean clustering,  EM clustering by MLE, Provable 2-step EM,mean-shift and landscape ]	Ch 10.1-10.4Handout	Lect 20.pdf

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