Machine Learning Techniques, Spring 2018

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)

datesyllabustodo/donesuggested reading
2/27topic 1: how can machines learn by embedding numerous features?
linear support vector machine
course slides; LFD e-8.1
3/6dual support vector machine course slides; LFD e-8.2
3/13kernel support vector machine course slides; LFD e-8.3
3/20soft-margin support vector machine homework 1 announced course slides; LFD e-8.4
3/27kernel logistic regression course slides;
extended reading:
4/3no class because of Spring Break
4/10topic 2: how can machines learn by combining predictive features?
blending and bagging
course slides;
extended reading:
4/17adaptive boosting course slides;
extended reading:
4/24decision treehomework 1 due; homework 2 announced; final project announced course slides;
extended reading:
5/1no class because of Labor Day (and yes we have a labor in this class)
5/8random forest course slides;
extended reading:
5/15gradient boosted decision tree course slides;
extended reading:
5/22topic 7: how can machines learn by distilling hidden features?
neural network
course slides; LFD e-7.1, e-7.2, e-7.3, e-7.4 (selected parts)
5/29(tentatively) deep neural network homework 2 due; homework 3 announced
6/5(tentatively) convolutional neural network
6/12(tentatively) recurrent neural network
6/19(tentatively) variational autoencoder
6/26finale and award ceremony homework 3 due
7/3summer vacation started (really?) final project due