Machine Learning, Spring 2023

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.


Course Information


Class Policy

Course Plan (tentative)

02/23 course introduction;
topic 1: when can machines learn?
basics of machine learning
03/02 the learning problems
03/09 topic 2: why can machines learn?
feasibility of learning
homework 1 announced
03/16 theory of generalization
03/23 topic 3: how can machines learn?
linear models
homework 2 announced
03/30 beyond basic linear models homework 1 due
04/06 topic 4: how can machines learn better?
combatting overfitting
homework 3 announced
04/13 combatting overfitting (2) homework 2 due
04/20 putting it altogether: support vector machine homework 4 announced; final project announced
04/27 soft-margin support vector machine homework 3 due
05/04 topic 5: how can machines learn by combining predictive features?
bagging and boosting
homework 5 announced
05/11 decision tree ensembles homework 4 due
05/18 topic 6: how can machines learn by distilling hidden features?
deep learning fundamentals
05/25 only two hours of class because of PAKDD
machine learning soundings
06/01 machine learning in practice / finale homework 5 due
06/08 no class and good luck with your final project final project due