Hsuan-Tien Lin

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Machine Learning, Fall 2015

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)

datesyllabustodo/donesuggested reading
9/14course introductioncourse slides
9/16topic 1: when can machines learn?
the learning problem
course slides; LFD 1.0, 1.1.1, 1.2.4
9/21learning to answer yes/nocourse slides; LFD 1.1.2, 3.1
9/23types of learningcourse slides; LFD 1.2
9/28no class because of Mid-Autumn Festival
9/30feasibility of learninghomework 1 announcedcourse slides; LFD 1.3
10/5topic 2: why can machines learn?
training versus testing
course slides; LFD 2.0, 2.1.1
10/7theory of generalizationcourse slides; LFD 2.1.2
10/12the VC dimensioncourse slides; LFD 2.2
10/14noise and errorhomework 1 due; homework 2 announcedcourse slides; LFD 1.4
10/19topic 3: how can machines learn?
linear regression
course slides; LFD 3.2
10/21logistic regression course slides; LFD 3.3
10/26linear models for classification course slides; LFD 3.3 (for SGD part only)
10/28nonlinear transformation homework 2 due; homework 3 announcedcourse slides; LFD 3.4
11/2topic 4: how can machines learn better?
hazard of overfitting
course slides; LFD 4.0, 4.1
11/4regularization course slides; LFD 4.2
11/9validation course slides; LFD 4.3
11/11three learning principles homework 3 due; homework 4 announcedcourse slides; LFD 5
11/16topic 5: how can machines learn by embedding numerous features?
linear support vector machine
course slides; LFD e-8.1
11/28dual support vector machinefinal project announced course slides; LFD e-8.2
11/23kernel support vector machine course slides; LFD e-8.3
11/25soft-margin support vector machine homework 4 due; homework 5 announced course slides; LFD e-8.4
11/30kernel logistic regression course slides;
extended reading:
12/2support vector regression course slides;
extended reading:
12/7topic 6: how can machines learn by combining predictive features?
blending and bagging
course slides;
extended reading:
12/9adaptive boosting homework 5 due; homework 6 announced course slides;
extended reading:
12/14decision tree course slides;
extended reading:
12/16random forest course slides;
extended reading:
12/21gradient boosted decision tree course slides;
extended reading:
12/23topic 7: how can machines learn by distilling hidden features?
neural network
homework 6 due; homework 7 announced course slides; LFD e-7.1, e-7.2, e-7.3, e-7.4 (selected parts)
12/28deep learning course slides; LFD e-7.6
12/30radial basis function network course slides; LFD e-6.3
1/4no class to enjoy your holidays and homework/project better (instructor office hour in R314)
1/6matrix factorization homework 7 due; homework 8 announced course slides;
1/11no class to fight for the final project in the last hours
1/13finale and award ceremony course slides
1/18no class because winter vacation started (really?)
1/20no class because winter vacation started (really?) homework 8 due; final project due

Last updated at CST 23:46, January 01, 2016
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