Hsuan-Tien Lin > Courses > Machine Learning, Fall 2014

Hsuan-Tien Lin

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Machine Learning, Fall 2014

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: Hsuan-Tien Lin (htlin AT csie . ntu . edu . tw) [office hour: after classes, or by appointment]
TAs and TA hour: ml2014ta AT csie . ntu . edu . tw
- Sheng-Chi You (Mondays 17:30, CSIE R536)
- Hong-Min Chu (Tuesdays 13:20, CSIE R536)
- You-Lin Tsou (Tuesdays 13:20, CSIE R536)
- Kuan-Hao Huang (Tuesdays 15:30, CSIE R536)
- Yu-Ping Wu (Tuesdays 15:30, CSIE R536)
- Chih-Wei Chang (Wednesdays 09:10, CSIE R536)
- Yi-An Lin (Thursdays 09:30, CSIE R536)

Course Information

Time: Mondays 10:20am to 11:40am; Wednesdays 10:20am to 11:40am
Room: CSIE Building, R103
Textbook: Learning from Data, by Yaser Abu-Mostafa, Malik Magdon-Ismail and Hsuan-Tien Lin
Language: Mandarin teaching with English slides
Grading: 70% homework, 30% project (tentative)
Full Course Website: http://ceiba.ntu.edu.tw/1031mlearn

Announcements

homework 6 announced on 12/21/2015, due on 01/07/2015
homework 5 announced on 12/03/2014, due on 12/17/2014
final project announced on 11/19/2014, due on 01/21/2015
homework 4 announced on 11/12/2014, due on 12/01/2014, extended to 12/03/2014
homework 3 announced on 10/29/2014, due on 11/17/2014
homework 2 announced on 10/15/2014, due on 10/29/2014
homework 1 announced on 10/01/2014, due on 10/15/2014
homework 0 announced on 9/14/2014, self-graded
policy and homework guidelines announced on 9/14/2014

Class Policy

main policy: here
homework guidelines: here; please note about the late submission part

Course Plan (tentative)

; LFD e-6.3

date	syllabus	todo/done	suggested reading
9/15	course introduction		course slides
9/17	topic 1: when can machines learn? the learning problem		course slides; LFD 1.0, 1.1.1, 1.2.4
9/22	learning to answer yes/no		course slides; LFD 1.1.2, 3.1
9/24	types of learning		course slides; LFD 1.2
9/29	feasibility of learning		course slides; LFD 1.3
10/1	topic 2: why can machines learn? training versus testing	homework 1 announced	course slides; LFD 2.0, 2.1.1
10/6	theory of generalization		course slides; LFD 2.1.2
10/8	the VC dimension		course slides; LFD 2.2
10/13	noise and error		course slides; LFD 1.4
10/15	topic 3: how can machines learn? linear regression	homework 1 due; homework 2 announced	course slides; LFD 3.2
10/20	logistic regression		course slides; LFD 3.3
10/22	linear models for classification		course slides; LFD 3.3 (for SGD part only)
10/27	nonlinear transformation		course slides; LFD 3.4
10/29	topic 4: how can machines learn better? hazard of overfitting	homework 2 due; homework 3 announced	course slides; LFD 4.0, 4.1
11/3	regularization		course slides; LFD 4.2
11/5	validation		course slides; LFD 4.3
11/10	three learning principles		course slides; LFD 5
11/12	no class and good luck with your other midterms	homework 4 announced; final project announced
11/17	topic 5: how can machines learn by embedding numerous features? linear support vector machine	homework 3 due	course slides; LFD e-8.1
11/19	dual support vector machine		course slides; LFD e-8.2
11/24	kernel support vector machine		course slides; LFD e-8.3
11/26	no class because of ACML
12/1	soft-margin support vector machine		course slides; LFD e-8.4
12/3	kernel logistic regression	homework 4 due; homework 5 announced	course slides; extended reading: Kernel Logistic Regression and the Import Vector Machine (Zhu and Hastie) A Note on Platt's Probabilistic Outputs for Support Vector Machines (Lin, Weng and Lin)
12/8	support vector regression		course slides; extended reading: SVM versus Least Squares SVM (Ye and Xiong) A Tutorial on Support Vector Regression (Smola and Scholkopf)
12/10	topic 6: how can machines learn by combining predictive features? blending and bagging		course slides; extended reading: A linear ensemble of individual and blended models for music rating prediction (Chen et al.) Bagging predictors (Breiman)
12/15	adaptive boosting		course slides; extended reading: A short introduction to boosting (Freund and Schapire)
12/17	decision tree	homework 5 due; homework 6 announced	course slides; extended reading: Classification and regression trees (overview of decision tree by Loh) Classification and regression trees (book of CART by Breiman et al.)
12/22	random forest		course slides; extended reading: Random forest (Breiman)
12/24	topic 7: how can machines learn by distilling hidden features? neural network		course slides; LFD e-7.1, e-7.2, e-7.3, e-7.4 (selected parts)
12/29	deep learning		course slides; LFD e-7.6
12/31	deep learning (cont'd)
1/5	radial basis function network		course slides
1/7	gradient boosted decision tree	homework 7 due	course slides; extended reading: Greedy Function Approximation: A Gradient Boosting Machine (Friedman)
1/12	summary		course slides
1/14	no class and good luck with your other finals
1/19	no class because winter vacation has started (really? :-) )	homework 7 due
1/21		final project report due

Last updated at CST 13:08, October 04, 2023
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