Class Schedule, Lecture Notes and Assignments (Spring 2003)
Please keep checking this page for updated course material for the week ! 



Homeworks 
Jan 14  Introduction to ML, Linear AlgebraÊ & UsefulÊ mathematical tools 

Matlab Primer 
Jan 21Ê  Fundamental Issues in Learning TheoryÊ I:Ê Optimization & Cost Functions, Complexity, Generalization 


Jan 28  Fundamental Issues in Learning TheoryÊ II:Ê
Inverse Problem, Functional Analysis & Kernel Representation 

Wiener Derivation 
Feb 04 
Catch Up Ê !! Project ÊGuidelines !! 


Feb 11  Bayesian Learning : Bayesian Inference, Maximum
Likelihood and EM algorithm, Density Estimation 

GaussMixEM 
Feb 18 
Supervised Learning I :Ê Linear Regression
Methods, Least Squares, BLUE Estimate, Shrinkage and subset selection. 

HW1, housing[data,names] 
Feb 25  Supervised Learning II : Linear Classification
methods LDA, QDA, Logistic Regression, Separating Hyperplanes 


Mar 04 
Supervised Learning III : Support Vector Machines,
Support Vector Regression,Ê Large Margin Methods 


Mar 11  Mid Term Examination (30%
Grades) 
(56pm) Closed Book 

Mar 18 


Mar 25  Supervised Learning IV:Ê Nonparametric methods,
RBFs, Locally Weighted Learning, Kernel Methods 

HW2, [C1, C2].data 
Apr 01 
Class Cancelled 

Apr 08  Unsupervised Learning I: Data Preprocessing & Scaling, Dimensionality Reduction, PCA, Factor Analysis, LWPR 


Apr 15  Unsupervised Learning II: Entropy, Info Max., KL divergence, ICA & Blind Separation 

ICA paper 
Apr 22  Inductive & Analytical Learning: Symbolic
ML, EBG/EBL, Decision Trees Reinforcement Learning: Dynamic Programming, TDlearning, QLearning, REINFORCE, ActorCritic 
Lecture XII 
HW3,
[X,M].data, ica.m (HW3 : do not submit) 
Apr 29  Final Project Presentations (40% Grades) 
(58 pm) Attendance Mandatory !! 

May 06  Final Project Presentations (40% Grades)Ê 