Class Schedule, Lecture Notes and Assignments (Spring 2003)

Note: The course content is continuously modified based on the needs & interests of the class.
Please keep checking this page for updated course material for the week !
Topics Covered
Lecture Notes
Handouts &
Jan 14 Introduction to ML,
Linear Algebra  & Useful  mathematical tools
Lecture I
Linear Algebra,
Matlab Primer
Jan 21  Fundamental Issues in Learning Theory  I: 
Optimization & Cost Functions, Complexity, Generalization
Lecture II
Bias-Variance Handout
Jan 28 Fundamental Issues in Learning Theory  II: 
Inverse Problem, Functional Analysis & Kernel Representation
Lecture III
Reference Paper,
Wiener Derivation
Feb 04
Catch Up   !! Project  Guidelines !!

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

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

Feb 25 Supervised Learning II : Linear Classification methods- LDA, QDA, Logistic Regression, Separating Hyperplanes
Lecture V

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

Tutorial [class, regress], SVMseq
Mar 11 Mid Term Examination (30% Grades)
(5-6pm) Closed Book
Mar 18
Spring Break - No Class
Mar 25 Supervised Learning IV:  Nonparametric methods, RBFs, Locally Weighted Learning, Kernel Methods
Lecture VIII

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

Apr 08 Unsupervised Learning I: Data Preprocessing & Scaling, Dimensionality Reduction, PCA, Factor Analysis, LWPR
Lecture IX
 DimRed, LWPR
Apr 15 Unsupervised Learning II: Entropy, Info Max., KL divergence, ICA & Blind Separation
Lecture X
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 XI

Lecture XII

HW3, [X,M].data, ica.m
(HW3 : do not submit)
Apr 29 Final Project Presentations (40% Grades)
Project Guidelines
(5-8 pm) Attendance Mandatory !!
May 06 Final Project Presentations (40% Grades