Hsuan-Tien Lin

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Machine Learning, Fall 2008

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

datesyllabustodo/donesuggested reading
2008.09.18introduction/unlearnability homework 0 released;
policy explained and released
2008.09.25learnability/perceptrons homework 1 released;
homework guidelines announced
  • textbook, Subsec 2.6.1, Subsec 2.6.2
  • The PLR algorithm handout
  • Sec 1 and Sec 2 of Abu-Mostafa, 1989 (note the slightly different definitions of pi and nu; other Secs would be taught later)
2008.10.02perceptron/linear regression homework 1 due
  • textbook, Subsec 2.3.1, Sec 3.2
2008.10.09optimization/generalization homework 2 released
2008.10.16generalization/VC inequalityhomework 2 due
2008.10.23generalization/nearest neighborshomework 3 released
2008.10.30nearest neighbors/RBF Network/generalized linear modelhomework 3 due
  • textbook, Sec 5.1, Sec 6.7, Sec 13.3, Subsec 14.3.6
2008.11.06Neural Networkhomework 4 released
2008.11.13Neural Network/Bayesian(midterm week)
  • textbook, Sec 11.3, Sec 11.4
2008.11.20Bayesianhomework 4 due
  • textbook, Sec 8.3
2008.11.27Bayesian/Ensemble homework 5 released
  • textbook, Sec 8.3
2008.12.04Bagging/Boostinghomework 5 due
2008.12.11Boosting homework 6 released
2008.12.18SVM homework 6 due; final project announced
  • textbook, Sec 12.1, 12.2, 12.3
2008.12.25SVM homework 7 released
2009.01.01no classNew Year's Day (holiday)
2009.01.08summary; Q and Ahomework 7 due
2009.01.15no class(final week); final project due

Last updated at CST 14:55, September 25, 2020
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