Date 
Lecture 
Lecturer 
Slides 
Useful links 
HWs 



January 14 Tuesday
 Intro
 Aarti
 Slides 
January 16 Thursday
 MLE and MAP estimation
 Barnabas
 MLE & MAP Slides (part1)
MLE & MAP Slides (part2) 
Tail Bounds 
January 21 Tuesday
 MLE & MAP, Naive Bayes
 Barnabas
 Naive Bayes

January 23 Thursday
 Naive Bayes, Linear regression
 Barnabas, Aarti
 Linear Regression

January 28 Tuesday
 Linear regression
 Aarti

January 30 Thursday
 Polynomial regression, Logistic regression
 Aarti
 Logistic Regression

February 4 Tuesday
 Logistic Regression, Support Vector Machine
 Aarti, Barnabas
 Support Vector Machines,
SVM (Annotated) 
Kernel Methods
 HW1 HW1_tex HW1 handout 
February 6 Thursday
 Support Vector Machines
 Barnabas

 Convex Optimization

February 11 Tuesday
 Support Vector Machines
 Barnabas
 

Project proposal 
February 13 Thursday
 Support Vector Machines, kernel density estimation
 Barnabas, Aarti
 Nonparametric Methods

February 18 Tuesday
 kNN, kernel regression
 Aarti
 


February 20 Thursday
 Model selection, crossvalidation
 Aarti
 Model Selection
 
HW1 due 
February 25 Tuesday
 kmeans clustering, MoG, ExpectationMaximization
 Barnabas
 ExpectationMaximization,
EM (Annotated)

Max Welling's classnotes

HW2 HW2_tex

February 27 Thursday
 ExpectationMaximization
 Barnabas
 
Tom Minka's notes

March 4 Tuesday
 Hidden Markov Models
 Barnabas
 Hidden Markov Models

March 6 Thursday
 Hidden Markov Models, Decision Trees
 Barnabas, Aarti
 Decision Trees
 

March 11 Tuesday
 No Class  spring break

 

HW2 due (March 10) 
March 13 Thursday
 No Class  spring break


March 18 Tuesday
 Decision Trees, Graphical Models
 Aarti
 Graphical Models (I)
 
HW3 HW3_tex

March 20 Thursday
 Graphical Models
 Aarti
 Graphical Models (II)

March 25 Tuesday
 Graphical Models
 Aarti
 Graphical Models (III)
 
Project Midterm (Mar 26) 
March 27 Thursday
 Principal Component Analysis
 Barnabas
 PCA

April 1 Tuesday
 Independent Component Analysis
 Barnabas
 ICA

April 3 Thursday
 Boosting
 Aarti
 Boosting
 
HW3 due 
April 8 Tuesday
 Midterm


April 10 Thursday
 No Class  spring carnival


April 15 Tuesday
 Markov Chain Monte Carlo methods
 Barnabas
 MCMC

April 17 Thursday
 Learning Theory (I)
 Aarti
 PACBounds
 
HW4 out 
April 22 Tuesday
 Learning Theory (II)
 Aarti
 VCBounds

April 24 Thursday
 Neural Networks
 Aarti
 Neural Networks

April 29 Tuesday
 Deep learning
 Barnabas
 Deep Learning

 HW4 Due (Apr 27)

May 1 Thursday
 No lecture (Poster presentations)



 Project Demo (NSH Atrium, 2.30pm  5.30pm)
