Machine Learning Foundations, Fall 2019

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.


Course Information


Class Policy

Course Plan (tentative)

datesyllabustodo/donesuggested reading
09/13 no class because of mid-autumn festival
09/20 course introduction;
topic 1: when can machines learn?
the learning problem
course slides;
course slides; LFD 1.0, 1.1.1, 1.2.4
09/27 learning to answer yes/no course slides; LFD 1.1.2, 3.1
10/04 types of learning homework 1 announced course slides; LFD 1.2; LFD 1.3
10/05 no class on this make-up day because it will not be effective to take four hours within two days
10/11 no class because of long weekend of double-ten holiday
10/18 feasibility of learning
course slides; LFD 1.3
10/25 topic 2: why can machines learn?
training versus testing;
theory of generalization
course slides; LFD 2.0, 2.1.1;
course slides; LFD 2.1.2
11/01 the VC dimension homework 1 due course slides; LFD 2.2
11/08 noise and error homework 2 announced course slides; LFD 1.4
11/15 no class because of NTU birthday
11/22 topic 3: how can machines learn?
linear regression
course slides; LFD 3.2
11/29 logistic regression course slides; LFD 3.3
12/06 linear models for classification course slides; LFD 3.3 (for SGD part only)
12/13 nonlinear transformation homework 2 due course slides; LFD 3.4
12/20 topic 4: how can machines learn better?
hazard of overfitting
homework 3 announced course slides; LFD 4.0, 4.1
12/27 regularization course slides; LFD 4.2
01/03 validation course slides; LFD 4.3
01/10 three learning principle course slides; LFD 5
01/14 (note: Tuesday) winter vacation begins (really?) homework 3 due