Tentative Schedule
description
Transcript of Tentative Schedule
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