Hsuan-Tien Lin

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Machine Learning, Fall 2011

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.

People

Instructor Head TA TA TA TA TA TA TA
Name Hsuan-Tien Lin Ku-Chun Chou Polone Chen Yu-Cheng Chou Wei-Yuan Shen Chun-Yen Ho Chung-Liang Li Yi-Hung Huang
Email htlin
AT csie . ntu . edu . tw
ml2011ta
AT csie . ntu . edu . tw
Office Hour after class in R102 or by appointment Tuesdays 6:30pm to 7:30pm in R105; Fridays 10:00am to 11:00am in R217
Picture Hsuan-Tien Lin Ku-Chun Chou Polone Chen Yu-Cheng Chou Wei-Yuan Shen Chun-Yen Ho Chung-Liang Li Yi-Hung Huang

Course Information

Announcements

Class Policy

Course Plan (tentative)

datesyllabustodo/donesuggested reading
2010.09.12Mid-Autumn Festival (no class) homework 0 released; policy released
2010.09.19introduction homework 1 released; policy explained
  • taught in class: Ch01 Opening, Sec 1.2 (except Subsec 1.2.2)
  • self-reading: Sec 1.1, Subsec 1.2.2
2010.09.26introduction reasons to stay in class
  • taught in class: more on Sec 1.2, Sec 1.3
  • self-reading: Subsec 1.2.2
2010.10.03introduction/generalization homework 1 due; homework 2 released; sidework 1 released
  • taught in class: Sec 1.4 (selected)
  • self-reading: Subsec 1.4.1, 1.4.3, 1.4.4
2010.10.10Double-Ten Holiday (no class)
2010.10.17generalization homework 2 due
  • taught in class: Ch02 opening, Sec 2.1 opening, Subsec 2.1.1 (selected)
  • self-reading: Example 2.2
2010.10.24generalization homework 3 released
  • taught in class: Sec 2.3 opening, Subsec 2.1.2, 2.1.3
  • self-reading: Subsec 2.1.4, 2.3.1, 2.3.2
  • recommended reading: Sec 2.2
2010.10.31generalization/linear model
  • taught in class: Sec 2.3 (selected), Sec 3.1, 3.2 (until page 3-9)
  • self-reading: Subsec 2.3.3, Example 3.1
2010.11.07linear model homework 4 released; good luck with your other midterms
  • taught in class: Sec 3.2, 3.3
  • self-reading: Ex 3.6 for the cross-entropy error, Ex 3.10 for PLA explanation, Prob 3.5 for Adaline explanation, Prob 3.14 for LinReg4Classification, Example 3.1, 3.2, 3.3
2010.11.14linear model/overfitting homework 3 due
  • taught in class: Sec 3.4, 3.5
  • self-reading: Example 3.4, Subsec 4.1.1 (and Ch04 opening)
2010.11.21regularization homework 4 due; homework 5 released
  • taught in class: Sec 4.1, 4.2 (selected)
  • self-reading: Example 4.1, 4.2, Subsec 4.2.3
2010.11.28validation/principles
  • taught in class: Sec 4.3, Ch 5 (selected)
  • self-reading: Subsec 4.3.4, Example 5.1, 5.2, 5.3
2010.12.05support vector machine homework 5 due; homework 6 released
  • taught in class: Sec 8.1 (except theory), beginning of Sec 8.2 (before Exercise 8.7)
  • self-reading: Exercise 8.2, Example 8.1
2010.12.12support vector machine
  • taught in class: Sec 8.2 (before Exercise 8.12)
  • self-reading: Examples 8.2, 8.3
2010.12.19support vector machine
2010.12.26aggregation homework 6 due (12/30); homework 7 released
2011.01.02aggregation
  • taught in class: page 20-end of LFD Slide Ch10, Sec 2.4 of LFD
2011.01.09summary competition ends (1/8); homework 7 due (1/13); final project due (1/13); good luck with your other finals

Last updated at CST 01:21, February 17, 2015
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