Hsuan-Tien Lin > Courses > Machine Learning, Fall 2015

Hsuan-Tien Lin

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Machine Learning, Fall 2015

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: ml2015ta AT csie . ntu . edu . tw
- You-Lin Tsou (CSIE R03): Mondays 14:00--15:00 in CSIE R536
- Hong-Min Chu (CSIE R04): Mondays 14:00--15:00 in CSIE R536
- Liang-Wei Chen (CSIE B01): Tuesdays 10:00--11:00 in CSIE R536
- Yao-Yuan Yang (CSIE B01): Tuesdays 10:00--11:00 in CSIE R536
- Yu-An Chung (CSIE B01): Thursdays 13:00--14:00 in CSIE R536
- Meng-Yuan Yang (CSIE B01): Thursdays 14:00--15:00 in CSIE BASEMENT
- Si-An Chen (CSIE B02): Thursdays 14:00--15:00 in CSIE BASEMENT
- Kuan-Hao Huang (CSIE R03): Fridays 10:40--11:40 in CSIE R536
- Hsien-Chun Chiu (CSIE R04): Fridays 16:00--17:00 in CSIE R536

Course Information

Time: Mondays 10:20 to 12:10; Wednesdays 10:20 to 12:10
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/1041mlearn

Announcements

homework 8 announced on 1/1/2016, due on 1/20/2016
homework 7 announced on 12/25/2015, due on 1/6/2015
final project announced on 11/26/2015 (lucky again), due on 01/20/2016
homework 6 announced on 12/10/2015, due on 12/23/2015
homework 5 announced on 11/26/2015 (the lucky day), due on 12/9/2015
homework 4 announced on 11/12/2015, due on 11/25/2015
homework 3 announced on 11/02/2015, due on 11/16/2015
homework guidelines updated on 10/15/2015
homework 2 announced on 10/15/2015, due on 11/02/2015
homework 1 announced on 9/30/2015, due on 10/14/2015
homework 0 announced on 9/13/2015, self-graded
policy and homework guidelines announced on 9/13/2015

Class Policy

Course Plan (tentative)

date	syllabus	todo/done	suggested reading
9/14	course introduction		course slides
9/16	topic 1: when can machines learn? the learning problem		course slides; LFD 1.0, 1.1.1, 1.2.4
9/21	learning to answer yes/no		course slides; LFD 1.1.2, 3.1
9/23	types of learning		course slides; LFD 1.2
9/28	no class because of Mid-Autumn Festival
9/30	feasibility of learning	homework 1 announced	course slides; LFD 1.3
10/5	topic 2: why can machines learn? training versus testing		course slides; LFD 2.0, 2.1.1
10/7	theory of generalization		course slides; LFD 2.1.2
10/12	the VC dimension		course slides; LFD 2.2
10/14	noise and error	homework 1 due; homework 2 announced	course slides; LFD 1.4
10/19	topic 3: how can machines learn? linear regression		course slides; LFD 3.2
10/21	logistic regression		course slides; LFD 3.3
10/26	linear models for classification		course slides; LFD 3.3 (for SGD part only)
10/28	nonlinear transformation	homework 2 due; homework 3 announced	course slides; LFD 3.4
11/2	topic 4: how can machines learn better? hazard of overfitting		course slides; LFD 4.0, 4.1
11/4	regularization		course slides; LFD 4.2
11/9	validation		course slides; LFD 4.3
11/11	three learning principles	homework 3 due; homework 4 announced	course slides; LFD 5
11/16	topic 5: how can machines learn by embedding numerous features? linear support vector machine		course slides; LFD e-8.1
11/28	dual support vector machine	final project announced	course slides; LFD e-8.2
11/23	kernel support vector machine		course slides; LFD e-8.3
11/25	soft-margin support vector machine	homework 4 due; homework 5 announced	course slides; LFD e-8.4
11/30	kernel logistic regression		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)
12/2	support vector regression		course slides; extended reading: SVM versus Least Squares SVM (Ye and Xiong) A Tutorial on Support Vector Regression (Smola and Scholkopf)
12/7	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)
12/9	adaptive boosting	homework 5 due; homework 6 announced	course slides; extended reading: A short introduction to boosting (Freund and Schapire)
12/14	decision tree		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.)
12/16	random forest		course slides; extended reading: Random forest (Breiman)
12/21	gradient boosted decision tree		course slides; extended reading: Greedy Function Approximation: A Gradient Boosting Machine (Friedman)
12/23	topic 7: how can machines learn by distilling hidden features? neural network	homework 6 due; homework 7 announced	course slides; LFD e-7.1, e-7.2, e-7.3, e-7.4 (selected parts)
12/28	deep learning		course slides; LFD e-7.6
12/30	radial basis function network		course slides; LFD e-6.3
1/4	no class to enjoy your holidays and homework/project better (instructor office hour in R314)
1/6	matrix factorization	homework 7 due; homework 8 announced	course slides; Matrix Factorization Techniques for Recommender Systems (Koren, Bell and Folinsky)
1/11	no class to fight for the final project in the last hours
1/13	finale and award ceremony		course slides
1/18	no class because winter vacation started (really?)
1/20	no class because winter vacation started (really?)	homework 8 due; final project due

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