| 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
| k-NN, kernel regression
| Aarti
| |
|
|
| February 20 Thursday
| Model selection, cross-validation
| Aarti
| Model Selection
| |
HW1 due |
| February 25 Tuesday
| k-means clustering, MoG, Expectation-Maximization
| Barnabas
| Expectation-Maximization,
EM (Annotated)
|
Max Welling's classnotes
|
HW2
HW2_tex
|
| February 27 Thursday
| Expectation-Maximization
| 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)
|
|
