Hsuan-Tien Lin > Courses > Machine Learning, Fall 2010

Hsuan-Tien Lin

Home | MOOCs | AIsk | Courses | Research Group | Awards | Publications | Presentations | Programs/Data

Machine Learning, Fall 2010

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

Instructor: Hsuan-Tien Lin (htlin AT csie DOT ntu DOT edu DOT tw)
TAs: Chen-Wei Hung, Yu-Xun Ruan, Yao-Nan Chen, Chao-Kai Chiang, Chia-Hsuan Wang and Yu-Chen Chou (ml10ta AT csie DOT ntu DOT edu DOT tw), with TA hours in R110 from 6pm to 7:30pm in the weeks before homework deadlines
Time: Mondays 2:20pm to 5:20pm
Room: R104
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/991ML

Announcements

homework 8 released on 12/27/2010; OPTIONAL, due at 4pm on 01/03/2011
Let's heat up the competition here
homework 7 released on 12/13/2010; due at 4pm on 12/27/2010
final project released on 11/29/2010; due at 3:10 pm on 01/10/2011
homework 6 released on 11/29/2010; due at 4pm on 12/13/2010
sidework 2 released on 11/26/2010; self-graded
deadline of homework 5 extended to 11/29/2010, TA hour changed to 11/25/2010 because of the midterm. Good luck!!
homework 5 released on 11/08/2010; due at 4pm on 11/22/2010
homework 4 released on 10/25/2010; due at 4pm on 11/08/2010
homework 3 released on 10/11/2010; due at 4pm on 10/25/2010
sidework 1 released on 10/01/2010; self-graded
homework 2 released on 09/27/2010; due at 4pm on 10/11/2010
homework 1 released on 09/13/2010; due at 4pm on 09/27/2010
policy released on 09/13/2010, as explained here
homework 0 released on 09/06/2010; self-graded

Class Policy

main policy: here, as explained here
a reminder that you must write your homework in English

Course Plan (tentative)

date	syllabus	todo/done	suggested reading
2010.09.06	(last week of summer!)	homework 0 released
2010.09.13	introduction/perceptrons	homework 1 released; policy explained here	taught in class: beginning of Ch 1 some of Sec 1.1 beginning of Sec 1.2 Subsec 1.2.1 reading assignment: Sec 1.1 Subsec 1.2.2
2010.09.20	perceptrons/setup/learnability	advantages announced	taught in class: Sec 1.3 beginning of Sec 1.4 some of Subsec 1.4.1 reading assignment: Subsec 1.4.1
2010.09.27	learnability/generalization	homework 1 due; homework 2 released; sidework 1 on the proof of Hoeffding's inequality released	taught in class: Subsec 1.4.1, 1.4.2 beginning of Ch 2 and Sec 2.1 reading assignment: Subsec 1.4.3
2010.10.04	generalization/VC dimension		taught in class: Page 2-4, 2-5, 2-6, 2-7 Definition 2.4 reading assignment: Page 2-1 to 2-7
2010.10.11	VC inequality/PLR revisited	homework 2 due; homework 3 released	taught in class: selected parts of Section 2.1 Page 3-1, 3-2, 3-3 reading assignment: whole Section 2.1 other suggested material: the note on VC proof given in class
2010.10.18	practical VC/linear classification		taught in class: Section 2.2 Section 3.1 and Stochastic Gradient Decent view of Adaline reading assignment: PLEASE spend some more time on homework 0 other suggested material: Section 2.3, which is yet another proof of the VC bound
2010.10.25	linear model	homework 3 due; homework 4 released	taught in class: selected parts of Sections 3.2 and 3.3 reading assignment: Sections 3.2, 3,3 and 3.4
2010.11.01	overfitting/regularization/validation		taught in class: selected parts of Sections 4.1, 4.2 and 4.4 Subsetion 4.3.1 reading assignment: Sections 3.4, 4.1, 4.2 Subsection 4.3.1
2010.11.08	validation/support vector machines	homework 4 due; homework 5 released	taught in class: selected parts of Sections 4.4 beginning of Chapter 8 (slides released via class email) reading assignment: Section 4.4 Chapter 5 pages 8-1 to 8-12 of the Chapter 8 slides
2010.11.15	(no class---NTU anniversary!)
2010.11.22	support vector machines	sidework 2 on KKT conditions released	taught in class: continuing on Chapter 8 (slides released via class email) reading assignment: pages 8-13 to 8-20 of the Chapter 8 slides
2010.11.29	support vector machines/practical tools	homework 5 due; final project announced; homework 6 released;	taught in class: continuing on Chapter 8 (slides released via class email) practical tools (for final project): scaling, missing values, parameter selection, large-scale data reading assignment: pages 8-21 and beyond of the Chapter 8 slides
2010.12.06	multiclass/aggregation/bagging		taught in class: multiclass classification via binary classification and regression (check the external slides) beginning of Chapter 10 (slides released via class email) reading assignment: pages 10-1 to 10-11 of the Chapter 10 slides
2010.12.13	boosting/decision trees	homework 6 due; homework 7 released	taught in class: middle of of Chapter 10 (slides released via class email) slides on AdaBoost reading assignment: pages 10-12 to 10-23 of the Chapter 10 slides
2010.12.20	decision trees/random forest/feature selection		taught in class: end of of Chapter 10 (slides released via class email) feature selection reading assignment: pages 10-23 to 10-29 of the Chapter 10 slides
2010.12.27	Bayesian approaches	homework 7 due; optional homework 8 released	taught in class: Maximum Likelihood Maximum Posterior (MAP) Bayes Estimator
2011.01.03	Bayesian approaches	homework 8 due	taught in class: Gaussian Discriminant Analysis and its connection to Linear Classification Naive Bayes Classifier contrasting Bayesian with other approaches
2011.01.10	summary/final project discussions	final project due	taught in class: summary of what we have taught final project awards

Last updated at CST 13:07, October 04, 2023
Please feel free to contact me: htlin.email.png