Hsuan-Tien Lin > Courses > Machine Learning, Fall 2011

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

Course Information

Time: Mondays 1:20pm to 4:20pm
Room: R102
Textbook: Learning from Data, by Yaser Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin (draft version)
Language: English
Grading: 70% homework, 30% project (tentative)
Full Course Website: http://ceiba.ntu.edu.tw/1001ML

Announcements

scoring formula announced on 12/27/2011
deadline of final project extended to 1/13/2012
homework 7 released on 12/26/2011; due 1/13/2012
deadline of homework 6 extended to 12/30/2011
homework 6 released on 12/07/2011; due 12/26/2011
final project released on 12/05/2011; due 1/9/2012
homework 5 released on 11/21/2011; due 12/05/2011
homework 4 released on 11/07/2011; due 11/21/2011
deadline of homework 3 effectively extended to 11/14/2011
homework 3 released on 10/24/2011; due 11/07/2011
sidework 1 on Hoeffding's Inequality released on 10/4/2011
homework 2 released on 10/3/2011; due 10/17/2011
homework guidelines released on 9/27/2011, along with the update of homework 1 submission guidelines
homework 1 released on 9/19/2011; due 10/3/2011
policy released on 9/15/2011
homework 0 released on 9/13/2011; self graded

Class Policy

main policy: here, as explained here
a reminder that you must write your homework in English
For the late homework submissions, students should contact any of the TAs for the exact submission procedure and write down the submission time on the late parts clearly.

Course Plan (tentative)

date	syllabus	todo/done	suggested reading
2010.09.12	Mid-Autumn Festival (no class)	homework 0 released; policy released
2010.09.19	introduction	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
2010.09.26	introduction	reasons to stay in class	taught in class: more on Sec 1.2, Sec 1.3 self-reading: Subsec 1.2.2
2010.10.03	introduction/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
2010.10.10	Double-Ten Holiday (no class)
2010.10.17	generalization	homework 2 due	taught in class: Ch02 opening, Sec 2.1 opening, Subsec 2.1.1 (selected) self-reading: Example 2.2
2010.10.24	generalization	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
2010.10.31	generalization/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
2010.11.07	linear 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
2010.11.14	linear 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)
2010.11.21	regularization	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
2010.11.28	validation/principles		taught in class: Sec 4.3, Ch 5 (selected) self-reading: Subsec 4.3.4, Example 5.1, 5.2, 5.3
2010.12.05	support 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
2010.12.12	support vector machine		taught in class: Sec 8.2 (before Exercise 8.12) self-reading: Examples 8.2, 8.3
2010.12.19	support vector machine		taught in class: Sec 8.3 (and a quick discussion on the theory behind SVM) recommended reading: a practical guide to support vector classification
2010.12.26	aggregation	homework 6 due (12/30); homework 7 released	taught in class: page 1-19 of LFD Slide Ch10, AdaBoost elementary slides recommended reading: a short introduction to boosting
2011.01.02	aggregation		taught in class: page 20-end of LFD Slide Ch10, Sec 2.4 of LFD
2011.01.09	summary	competition ends (1/8); homework 7 due (1/13); final project due (1/13); good luck with your other finals	taught in class: summary, award ceremony

Last updated at CST 13:07, October 04, 2023
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