Stat 161/261
Introduction to Machine Learning:
Tues/Thurs: 11:00-12:15 am Spring 2014, Fowler A139.
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.
Reading Material
- E. Alpaydin: Introduction to
Machine Learning. MIT Press. (2nd edition). Note: this only
covers some of the material.
- Course notes (available on
line)
- Instructor: Prof. Alan Yuille,
yuille@stat.ucla.edu, 310-267-5383, office Math Sciences
8967.
- Office Hours: Wednesday
4.00-5.00 pm (8967 Math Sciences), or by appointment
(email).
- Syllabus.
- Recommended Books: (all books
have good points, but none cover the whole course).
- (i) Alpaydin. Introduction to
Machine Learning.
- (ii) Duda, Hart, Stork. Pattern
Classification.
- (iii) Hastie, Tibshirani,
Friedman. Elements of Statistical Learning.
- (iv) Bishop. Pattern
Recognition and Machine Intelligence.
- (v) Murphy. Machine Learning. A
Probabilistic Perspective.
Grading Plan: 4 homework assignments (60% ) 1 final
exam (40%)
Homework 1: homework1
Due Thursday 24/April
Homework 2: homework 2 Due
Tuesday 13/May
P1_train.txt P1_test.txt P3.text P3_outlier.txt P4_train.txt P4_test.txt
Homework 3: homework 3 Due
Tuesday 27/May
P3_A.txt P3_B.txt
Homework 4: homework 4
Due Thursday 6/June
Please email wjyouch@gmail.com for questions on the
homework
Tentative Schedule.
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Lecture
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Date
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Topics
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Reading Materials
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Handouts
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1
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April-1
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Introduction to the Machine Learning:
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Alpaydin: Chp 1.
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HandNotes1 BigDataHype?
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2
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April-3
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Bayes Decision Theory
Generalization and VC dimension
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Alpaydin: Chp 2.
2.1, 2.2,2.3, Chp 3.
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HandNotes2 RRLatexLecture2
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3
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April-8
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ROC and Precision/Recall Curves
Curse of Dimensionality
Bias and Variance
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Same Chps.
Also Chp
4.3,4.4,
4.7,4.8
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HandNotes3 RRLatexLecture3
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4
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April-10
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Learning Parametric Distributions
Exponential Models
Sufficeint Statistics
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Chp
4.1,4.2
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HandNotes4Revised RRLatexLecture4
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5
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April-15
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Continuation of Previous Lecture
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Previous lecture
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6
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April-17
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Non-Parametric Methods
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Chp 8
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HandNotes5
LatexLecture5
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7
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April-22
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Regression
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Chp 4.6, 5.8
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HandNotes6 LatexLecture6
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8
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April-24
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AdaBoost
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Chp 6.4,6.5
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HandNotes7
LatexLecture7
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9
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April-29
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Perceptron and Support Vector Machines
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PrimalDual
Chp 10.9
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HandNotes8 LatexLecture8
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10
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May-1
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The Kernel Trick
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HandNotes9
LatexLecture9 DecisionTree
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11
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May-6
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PCA and Fisher's LDA
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HandNotes10
LatexNotes10
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12
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May-8
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KernelPCA
MDS
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KernelPCA MultiDimScale LatexNotes11
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13
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May-13
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Nonlinear Dimension Reduction
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Saul1 Saul2 Saul3 Saul4 LatexNotes12
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14
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May-15
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Independent Component Anslysis
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ICAreview
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15
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May-20
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K-Means and EM Algorithm
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LatexNotes13
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16
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May-22
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Prob Models on Graphs (1)
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LatexNotes14
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17
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May-27
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Probability Models on Graphs (2)
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Same as previous lecture
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18
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May-29
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Latent SVM, PAC Theory
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LatexNotes15
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19
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June-3
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Course Review
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20
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June-5
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Homework Problem Review
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