Hsuan-Tien Lin

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Machine Learning, Fall 2014

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.

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Course Information

Announcements

Class Policy

Course Plan (tentative)

; LFD e-6.3
datesyllabustodo/donesuggested reading
9/15course introductioncourse slides
9/17topic 1: when can machines learn?
the learning problem
course slides; LFD 1.0, 1.1.1, 1.2.4
9/22learning to answer yes/nocourse slides; LFD 1.1.2, 3.1
9/24types of learningcourse slides; LFD 1.2
9/29feasibility of learningcourse slides; LFD 1.3
10/1topic 2: why can machines learn?
training versus testing
homework 1 announcedcourse slides; LFD 2.0, 2.1.1
10/6theory of generalizationcourse slides; LFD 2.1.2
10/8the VC dimensioncourse slides; LFD 2.2
10/13noise and errorcourse slides; LFD 1.4
10/15topic 3: how can machines learn?
linear regression
homework 1 due; homework 2 announced course slides; LFD 3.2
10/20logistic regression course slides; LFD 3.3
10/22linear models for classification course slides; LFD 3.3 (for SGD part only)
10/27nonlinear transformation course slides; LFD 3.4
10/29topic 4: how can machines learn better?
hazard of overfitting
homework 2 due; homework 3 announced course slides; LFD 4.0, 4.1
11/3regularization course slides; LFD 4.2
11/5validation course slides; LFD 4.3
11/10three learning principles course slides; LFD 5
11/12no class and good luck with your other midtermshomework 4 announced; final project announced
11/17topic 5: how can machines learn by embedding numerous features?
linear support vector machine
homework 3 due course slides; LFD e-8.1
11/19dual support vector machine course slides; LFD e-8.2
11/24kernel support vector machine course slides; LFD e-8.3
11/26no class because of ACML
12/1soft-margin support vector machine course slides; LFD e-8.4
12/3kernel logistic regressionhomework 4 due; homework 5 announced course slides;
extended reading:
12/8support vector regression course slides;
extended reading:
12/10topic 6: how can machines learn by combining predictive features?
blending and bagging
course slides;
extended reading:
12/15adaptive boosting course slides;
extended reading:
12/17decision treehomework 5 due; homework 6 announced course slides;
extended reading:
12/22random forest course slides;
extended reading:
12/24topic 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)
12/29deep learning course slides; LFD e-7.6
12/31deep learning (cont'd)
1/5radial basis function network course slides
1/7gradient boosted decision treehomework 7 due course slides;
extended reading:
1/12summary course slides
1/14no class and good luck with your other finals
1/19no class because winter vacation has started (really? :-) )homework 7 due
1/21final project report due

Last updated at CST 06:55, May 23, 2015
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