Hsuan-Tien Lin

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Machine Learning, Fall 2009

Course Description

Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the data observed. This course introduces the basics of learning theories, the design and analysis of learning algorithms, and some applications of machine learning.

Course Information

Announcements

Class Policy

Course Plan (tentative)

datesyllabustodo/donesuggested reading
2009.09.14introduction taught in class:
  • Page 1-1 of LFD
  • Page 1-4 of LFD
reading assignments:
  • Section 1.1 of LFD
2009.09.21introduction/perceptrons/learnability taught in class:
  • Subsection 1.2.1 of LFD
  • Section 1.3 of LFD (partially)
  • Page 1-20 of LFD
reading assignments:
  • Subsection 1.2.2 of LFD
  • Section 1.3 of LFD
2009.09.28generalization taught in class:
  • Subsection 1.4.1, 1.4.2, and 1.4.3 (parts) of LFD
reading assignments:
  • Subsection 1.4.3 of LFD
2009.10.05generalization/VC inequality homework 2 released taught in class:
  • Pages 2-4 to 2-7 of LFD
reading assignments:
  • Pages 2-1 to 2-4 of LFD
2009.10.12VC inequality/PLR resolved homework 2 due taught in class:
  • Pages 2-8 to 2-12 of LFD
  • The VC proof (as handout in class)
  • Proof of PLR convergence (on board)
reading assignments:
  • Pages 2-13 to 2-14 of LFD
  • Subsection 2.1.3 of LFD
  • Section 2.2 of LFD (will be discussed later)
2009.10.19linear model homework 3 released taught in class:
  • Pages 3-1 to 3-5 of LFD
  • linear programming, gradient descent, and stochastic gradient descent for linear classification
reading assignments:
  • Example 3.1 of LFD
2009.10.26linear model homework 3 due taught in class:
  • Pages 3-8 to 3-10 of LFD
reading assignments:
  • Pages 3-11 to 3-13 of LFD
2009.11.02linear model/overfitting homework 4 released taught in class: reading assignments:
  • Example 3.2 of LFD
  • Section 3.4 of LFD
  • Subsections 4.2.3 and 4.3.2 of LFD
2009.11.09overfitting/Neural Network good luck with your other midterms taught in class:
  • Chapter 5 of LFD
  • Notes on Neural Network (to be announced thru email)
2009.11.16Neural Network homework 4 due taught in class:
  • Notes on Neural Network (to be announced thru email)
reading assignments:
2009.11.23Neural Network/Support Vector Machine homework 5 released taught in class:
  • Notes on Neural Network (to be announced thru email)
  • SVM Introduction
2009.11.30Bayesian and Unsupervised by Prof. Shou-de Lin taught in class:
2009.12.07Unsupervised and Machine Discovery by Prof. Shou-de Lin homework 5 due taught in class:
2009.12.14Bayesian/Support Vector Machine final project announced
2009.12.21Support Vector Machine/Bagging/Boosting homework 6 released
2009.12.28Boosting homework 6 due; homework 7 released taught in class:
2010.01.04Boosting/Summary homework 7 due
2010.01.11Summary/Final Project Discussions final project due
good luck with your other finals
taught in class:

Last updated at CST 01:20, February 17, 2015
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