Lecture No. Date Day Topic

1 05-08 Wednesday Introduction to ML

2 06-08 Thursday Probability

3 07-08 Friday Statistical Decision Theory - 1

4 12-08 Wednesday Linear Algebra

5 13-08 Thursday Convex Optimization

6 14-08 Friday Bayesian Learning - 1 (ML, MAP, Bayes estimates, Conjugate priors)

7 19-08 Wednesday Statistical Decision Theory - 2

8 21-08 Friday Linear Regression

9 26-08 Wednesday Shrinkage Methods + Principal Component Analysis

10 28-08 Friday Linear Classification - 1

11 02-09 Wednesday Logistic Regression + Linear Discriminant Analysis and QDA

12 04-09 Friday Linear Classification - 2 (Perceptron)

13 09-09 Wednesday Support Vector Machines + Kernels

14 11-09 Friday Artificial Neural Networks + BackPropagation

15 16-09 Wednesday Decision Trees

16 18-09 Friday Decision Trees + RBF Neural Networks Tutorial

17 23-09 Wednesday Evaluation measures + Hypothesis testing

18 28-09 Monday Hypothesis Testing

19 30-09 Wednesday Ensemble Methods (Bagging, Adaboost, Gradient Boosting)

20 07-10 Wednesday Clustering - 1 (K-means, K-medoids, Density-based, Hierarchical)

21 09-10 Friday Clustering - 2 (Spectral)

22 14-10 Wednesday Decision Trees Contd. + Density-based clustering

23 16-10 Friday Bayesian Learning - 2 (Bayes Optimal Classifier, Naive Bayes) + Gradient Boosting

24 21-10 Wednesday Intro to Graphical Models and Bayesian Networks

25 23-10 Friday Variable Elimination + MRF

26 28-10 Wednesday Expectation Maximization and GMMs

27 30-10 Friday Expectation Maximization - 2

28 04-11 Wednesday Learning theory

29 06-11 Friday Frequent Pattern mining

30 13-11 Wednesday Intro to Reinforcement Learning