Stat 161/261

Introduction to Machine Learning:
 
Mon/Wed/Fri: 3:00-3:50am Spring 2010, Math/Sciences 5203.
 
www.stat.ucla.edu/~yuille/Courses/
 

Course Description

This course gives an accessible introduction to pattern analysis and machine intelligence aimed at advanced undergraduates and graduate students. (It is simpler than the more advanced class Stat 231/CS 276 -- you cannot take both courses for graduate credit).

Reading Material

Grading Plan: homework assignments (60% ), computer assignments (40%).
Homework 1:  homework1
Homework 2:  homework 2
Homework 3: homework 3

Tentative Schedule.

Lecture

Date

Topics

Reading Materials

Handouts

1

March-29

Introduction to the Machine Learning:
 

Alpaydin: Chp 1.

2010lecture1.pdf

2

March-31

Supervised Learning:
 

Alpaydin: Chp 2.

2.1

   2010lecture2.pdf

3

April-2

VC -- Dimension
 

Alpaydin: Chp 2.

2.2-2.3

   2010lecture3.pdf

4

April-5

            
  Supervised Learning: (cont)
  

Alpaydin: Chp 2
2.4-2.8

  2010lecture4.pdf

5

April-7

Bayesian Decision Theory 

Alpaydin: Chp 3.

   2010lecture5.pdf
       6
                April-9
   
Parametric Methods
                 Alpaydin: Chp 4 2010lecture6.pdf

     7

April-12

                   
More Parametric Methods 
 

Alpaydin: Chp 4

    2010lecture7.pdf

8

April-14

                    
         Parametric Methods (cont)       
 

                  Alpaydin; Chp 4

    2010lecture8.pdf
       9
                April-16
Multivariate Methods
                 Alpaydin Chp 5.
    2010lecture9.pdf

    10

April-19

           Dimension Reduction:
Principle Component Analysis
                Alpaydin Chp 6.    2010lecture10.pdf

11

April-21

 

            Dimension Reduction:

       Singular Value Decomposition
                Alpaydin Chp 6.
 Same as previous lecture
      12
                April-23
          Dimension Reduction:
       Fisher's LDA
                  Alpaydin Chp 6
   2010lecture11.pdf

13

April-26

                Clustering
                 Alpaydin: Chp 7  2010lecture12.pdf
    14

April-28

 
                  Clustering     
 

Alpaydin: Chp 7

Same as previous lecture
      15
                April-30
Non-Parameteric Methods
                Alpaydin: Chp 8  2010lecture13.pdf

16

May-3

 
Decision Trees

Alpaydin: Chp 9

    2010lecture14.pdf

17

May-5

         
Linear Discrimination

Alpaydin: Chp 10

   2010lecture15.pdf
      18
                 May-7
Linear Discrimination, Support Vectors
and the Kernel Trick
                Alpaydin: Chp 10 Same as previous lecture

19

May-10

Support Vectors in Primal Space 

Guest Lecture: Prof. Y-N Wu. 


    20

              May-12

 Multi-Layer Perceptrons

 Alpaydin: Chp 11

   2010lecture16.pdf
    21

              May-14

 AdaBoost

 Alpaydin: Ch 15.5

  2010lecture17.pdf
    22
              May-17
 AdaBoost (cont)

 

2010lecture18.pdf

23

              May-19
MultiClass SVM 

 

  2010lecture19.pdf

24

              May-21
 Hidden Markov Models
              Alpaydin: Ch 13    2010lecture20.pdf

    25

              May-24
Hidden Markov Models (cont)
              Alpaydin: Ch 13            

Lecture 20 notes 

      26
               May-26
     
Hidden Markov Models (cont)
             Alpaydin: Ch 13      Lecture 20 notes

      27
               May-28
 
 Latent SVM

2010lecture21.pdf


               May-31
  
Memorial Day



      28
               June-2
Reinforcement Learning
           Alpaydin Ch 15
 2010lecture22.pdf

      29
               June-4
          
Review