Course Schedule

Week	Topics
1	Classification and Regression Problems
2	Bayesian and Frequentist Approaches
3	Probably Approximately Correct Learning and Empirical Risk Minimization
4	Empirical Risk Minimization, Uniform Convergence in WLLN
5	Vapnik-Chervonenkis Dimension, Characterization of UWLLN
6	Computational Learning Theory
7	Representation Capability of Neural Networks
8	Convex Sets and Convex Functions
9	Support Vector Machines, Boosting, Gaussian Process based Learning
10	Decision Trees, Random Forests and Clustering
11	Recurrent Neural Networks, Seq2Seq Models
12	Attention Mechanisms, BERT, GPT