Hsuan-Tien Lin

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Machine Learning, Fall 2013

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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Class Policy

Course Plan (tentative)

datesyllabustodo/donesuggested reading
9/9course introduction
9/12the learning problem
9/16learning to answer yes/no
9/19no class because of mid-autumn festival
9/23types of learning
9/26feasibility of learninghomework 1 announced
9/30training versus testing
10/3theory of generalization
10/7the VC dimension
10/10no class because of double-ten festivalhomework 2 announced
10/14noise and errorhomework 1 due
10/17linear regression
10/21logistic regression
10/24linear models for classification
10/28nonlinear transformationhomework 2 due; homework 3 announced
10/31hazard of overfitting
11/4regularization
11/7validation
11/11three learning principles (instructor not in class because of ACML)homework 4 announced
11/14no class because of ACML
11/18linear support vector machinehomework 3 due
11/21dual support vector machine
11/25kernel support vector machine
11/28soft-margin support vector machinehomework 4 due; final project announced
12/2kernel models for logistic regression
12/5kernel models for regressionhomework 5 announced
12/9aggregation models: blending and bagging
12/12aggregation models: adaptive boosting
12/16aggregation models: decision tree
12/19aggregation models: random foresthomework 5 due; homework 6 announced
12/23feature extraction models: neural network
12/26feature extraction models: deep learning
12/30feature extraction models: RBF network
1/2misc models
1/6homework 6 due; final project award ceremony
1/9no class and good luck with your other finals
1/3no class because winter vacation started (really? :-) )
1/16final project report due

Last updated at CST 01:21, February 17, 2015
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