Hsuan-Tien Lin > Courses > Machine Learning Foundations, Fall 2017

Hsuan-Tien Lin

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Machine Learning Foundations, Fall 2017

Course Description

Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. The course teaches the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know.

People

instructor: Hsuan-Tien Lin (htlin AT csie . ntu . edu . tw) [office hour: after classes, or by appointment]
TAs and TA hour: ml2017ta AT csie . ntu . edu . tw
- Bo-Yo Chen, CSIE R05: Mondays 9:00-10:00, CSIE R536
- I-Ting Chen, CSIE R06: Wednesdays 16:00-17:00, CSIE R536
- Chia-You Chen (volunteer), CSIE B04: Thursdays 18:30-19:30, Red Sofa at CSIE Basement
- Kuen-Han Tsai, CSIE R06: Fridays 16:00-17:00, CSIE R536

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: 100% homework (tentative)
Full Course Website: http://ceiba.ntu.edu.tw/1061mlfound

Announcements

homework 4 announced on 1/2/2018, due on 1/16/2018
homework 3 announced on 12/19/2017, due on 1/2/2018
homework 2 announced on 11/14/2017, due on 12/12/2017
homework 1 announced on 10/04/2017, due on 11/14/2017
homework 0 announced on 08/24/2017, self-graded
policy announced on 08/24/2017

Class Policy

main policy

Course Plan (tentative)

date	syllabus	todo/done	suggested reading
09/12	course introduction; topic 1: when can machines learn?		course slides
09/19	the learning problem		course slides; LFD 1.0, 1.1.1, 1.2.4
09/26	learning to answer yes/no types of learning		course slides; course slides; LFD 1.1.2, 3.1; LFD 1.2
10/03	(unfinished parts last week); feasibility of learning	homework 1 announced	course slides; LFD 1.3
10/10	no class because of double-ten holiday
10/17	no class because of DSAA conference
10/24	topic 2: why can machines learn? (unfinished parts last week); training versus testing		course slides; LFD 2.0, 2.1.1
10/31	(unfinished parts last week); theory of generalization		course slides; LFD 2.1.2
11/07	no class because of midterm (good luck!)
11/14	(unfinished parts last week); the VC dimension	homework 1 due; homework 2 announced	course slides; LFD 2.2
11/21	(unfinished parts last week); noise and error		course slides; LFD 1.4
11/28	topic 3: how can machines learn? linear regression		course slides; LFD 3.2
12/05	logistic regression		course slides; LFD 3.3
12/12	linear models for classification	homework 2 due	course slides; LFD 3.3 (for SGD part only)
12/19	nonlinear transformation	homework 3 announced	course slides; LFD 3.4
12/26	topic 4: how can machines learn better? hazard of overfitting		course slides; LFD 4.0, 4.1
01/02	regularization; validation	homework 3 due; homework 4 announced	course slides; course slides; LFD 4.2, 4.3
01/09	three learning principle		course slides; LFD 5
01/16	winter vacation begins (really?)	homework 4 due

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