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

Hsuan-Tien Lin

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Machine Learning Techniques, Spring 2017

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: ml2016ta AT csie . ntu . edu . tw
- Si-An Chen (CSIE R05): Mondays 11:20--12:10 (R536)
- Cheng-Yu Hsieh (CSIE R05): Wednesdays 10:20--11:10 (R536)
- Yu-Shao Peng (CSIE R05): Wednesdays 14:20--15:10 (R536)
- Boyo Chen (CSIE R05): Thursdays 10:20--11:10 (R536)
- Kuen-Han Tsai (CSIE B02): Thursdays 18:20--19:20 (CSIE Basement)

Course Information

Time: Tuesdays 12:20 to 14:10
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/1052mlearn

Announcements

Homework 4 announced on 05/23/2017, due on 06/20/2017
Final Project announced on 05/09/2017, due on 06/27/2017
Homework 3 announced on 05/02/2017, due on 05/23/2017
Homework 2 announced on 04/19/2017, due on 05/09/2017
Homework 1 announced on 03/21/2017, due on 04/11/2017

Class Policy

Course Plan (tentative)

date	syllabus	todo/done	suggested reading
2/21	topic 1: how can machines learn by embedding numerous features? linear support vector machine		course slides; LFD e-8.1
2/28	no class because of Massacre Rememberance Day
3/7	dual support vector machine		course slides; LFD e-8.2
3/14	kernel support vector machine		course slides; LFD e-8.3
3/21	soft-margin support vector machine	homework 1 announced	course slides; LFD e-8.4
3/28	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)
4/4	no class because of Spring Break
4/11	support vector regression	homework 1 due; homework 2 announced; final project announced	course slides; extended reading: SVM versus Least Squares SVM (Ye and Xiong) A Tutorial on Support Vector Regression (Smola and Scholkopf)
4/18	topic 2: 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)
4/25	adaptive boosting		course slides; extended reading: A short introduction to boosting (Freund and Schapire)
5/2	decision tree	homework 2 due; homework 3 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.)
5/9	random forest		course slides; extended reading: Random forest (Breiman)
5/16	gradient boosted decision tree		course slides; extended reading: Greedy Function Approximation: A Gradient Boosting Machine (Friedman)
5/23	topic 7: how can machines learn by distilling hidden features? neural network	homework 3 due; homework 4 announced	course slides; LFD e-7.1, e-7.2, e-7.3, e-7.4 (selected parts)
5/30	no class because of Dragon Boat Festival
6/6	deep learning		course slides; LFD e-7.6
6/13	radial basis function network		course slides; LFD e-6.3
6/20	matrix factorization and finale	homework 4 due	course slides; course slides Matrix Factorization Techniques for Recommender Systems (Koren, Bell and Folinsky)

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