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Introduction to Machine Learning.
Tues, Thurs: 11:00-12:15 am. Spring 2015. 
Classroom: Franz Hall 1260
Statistics Department (Math Sciences 8145), UCLA
http://www.stat.ucla.edu/~boyan/ml.html

Course Description

This course gives an accessible introduction to pattern analysis and machine intelligence aimed at advanced undergraduates and graduate students. 

  • Instructors: Boyan Bonev, Ph.D. bonev at ucla.edu; Vittal Premachandran, Ph.D. vittalp at ucla.edu Office: Slichter Hall 2847. Office hours: Thursday 4.00-5.00 pm
  • Teaching assistant: Peng Wang, Email: pengwangpku2012@gmail.com, Office: either Geology Bldg 5632 or Geology Bldg 4608. Office Hours: Friday 11-12 am
Grading Plan: 4 homework assignments (60% ), 1 final exam (40%).

Homeworks

Reading Material

  • Lecture notes by Prof. Alan Yuille (will be available online).
  • Ethem Alpaydin: Introduction to Machine Learning. MIT Press. (2nd edition).
  • The Elements of Statistical Learning Data Mining, Inference, and Prediction. - Trevor Hastie, Robert TibshiraniJerome Friedman  ( PDF )
  • Bayesian Reasoning and Machine Learning - David Barber ( PDF )
Online Resources


 Lecture  Date  Topics  Reading  Lecture notes
 1  Mar 31
 Introduction  Chp 1
 Slides
 2  Apr 2
 Bayes Decision Theory
 Chp 2
 LectureNotes2.pdf
 3  Apr 7
 ROC, Precision Recall, Bias-Variance
 Chp 4.3
 LectureNotes3.pdf
 4
 Apr 9
 (cont'd) Curse of Dimensionality, Bias-Var.

 Errata (Curse of Dim.)
 Errata (Pre-Rec)
 5  Apr 14
 Perceptron, Nonlinear Transformations and  Kernels
 Sections 1, 6, 7 and 8 of 
Introduction to Learning with Kernels
 Slides(Updated), Kernel  Notes
 6  Apr 16
 Kernels (cont'd) and Hard Margin SVM
 Sec 3.1,  3.2 of Reading Material  Slides, Clarification,  Perceptron and SVM
 7  Apr 21
 Exponential distributions and regression
 Chp 4;5
 LectureNotes4.pdf
 8  Apr 23
 Soft Margin SVMs  Sec 3.5 of Reading Material  Slides
 9  Apr 28
 Non-parametric methods  Chp 8  LectureNotes5.pdfSlides Note
 10  Apr 30
 Ensemble classifiers, Adaboost  Chp 6  LectureNotesAdaboost.pdf, Slides (136 pages)
 11
 
 May 5
 
 Decision Trees
 Tutorial     on     Decision     Forest  Slides
 12
 May 7
 PCA, SVD, Fisher LDA, ICA  Chp 6  LectureNotes6.pdfICA
 13  May 12
 Decision Trees (cont'd) and Random Forests
 Sec 9.2 of  ESL Book  (link to  PDF in  "Reading  Material"  above)  Slides
 14  May 14
 Dimensionality reduction. Fisher LDA; Non-linear methods
   Fisher (additional slides), LectureNotes14.pdf 
 15  May 19
 K-means
    Notes, Slides
 16  May 21
 GMM and EM

 Slides, Reading: Section 20.1-20.3 from David Barber's book (Link above)
 17  May 26
 Sparse Coding (Invited lecture: Dr. John Flynn)    John Flynn's notes
 18  May 28
 Probabilistic Models on Graphs
    GraphicalModels  (+Structured SVM , which is not covered)
 19 June 2
 Hidden Markov Models
  Slides; Rabiner's tutorial
 20 June 4
 Deep Networks
  Slides

Final exam: June 10, 3pm - 6pm, same classroom
- Bring a calculator. Closed book. There will be 10 questions.
<5 example questions in example.pdf>

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