Class Schedule, Lecture Notes and Assignments (Spring 2003)
Please keep checking this page for updated course material for the week ! |
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Homeworks |
Jan 14 | Introduction to ML, Linear AlgebraÊ & UsefulÊ mathematical tools |
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Matlab Primer |
Jan 21Ê | Fundamental Issues in Learning TheoryÊ I:Ê Optimization & Cost Functions, Complexity, Generalization |
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Jan 28 | Fundamental Issues in Learning TheoryÊ II:Ê
Inverse Problem, Functional Analysis & Kernel Representation |
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Wiener Derivation |
Feb 04 |
Catch Up Ê !! Project ÊGuidelines !! |
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Feb 11 | Bayesian Learning : Bayesian Inference, Maximum
Likelihood and EM algorithm, Density Estimation |
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GaussMixEM |
Feb 18 |
Supervised Learning I :Ê Linear Regression
Methods, Least Squares, BLUE Estimate, Shrinkage and subset selection. |
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HW1, housing[data,names] |
Feb 25 | Supervised Learning II : Linear Classification
methods- LDA, QDA, Logistic Regression, Separating Hyperplanes |
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Mar 04 |
Supervised Learning III : Support Vector Machines,
Support Vector Regression,Ê Large Margin Methods |
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Mar 11 | Mid Term Examination (30%
Grades) |
(5-6pm) Closed Book |
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Mar 18 |
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Mar 25 | Supervised Learning IV:Ê Nonparametric methods,
RBFs, Locally Weighted Learning, Kernel Methods |
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HW2, [C1, C2].data |
Apr 01 |
Class Cancelled |
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Apr 08 | Unsupervised Learning I: Data Preprocessing & Scaling, Dimensionality Reduction, PCA, Factor Analysis, LWPR |
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Apr 15 | Unsupervised Learning II: Entropy, Info Max., KL divergence, ICA & Blind Separation |
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ICA paper |
Apr 22 | Inductive & Analytical Learning: Symbolic
ML, EBG/EBL, Decision Trees Reinforcement Learning: Dynamic Programming, TD-learning, Q-Learning, REINFORCE, Actor-Critic |
Lecture XII |
HW3,
[X,M].data, ica.m (HW3 : do not submit) |
Apr 29 | Final Project Presentations (40% Grades) |
(5-8 pm) Attendance Mandatory !! |
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May 06 | Final Project Presentations (40% Grades)Ê |