With the increased availability of data from varied sources there has been increasing attention paid to the various data driven disciplines such as analytics and machine learning. In this course we intend to introduce some of the basic concepts of machine learning from a mathematically well motivated perspective. We will cover the different learning paradigms and some of the more popular algorithms and architectures used in each of these paradigms.
Week No.
Topics
1.
Introductory Topics
2.
Linear Regression and Feature Selection
3.
Linear Classification
4.
Support Vector Machines and Artificial Neural Networks
5.
Bayesian Learning and Decision Trees
6.
Evaluation Measures
7.
Hypothesis Testing
8.
Ensemble Methods
9.
Clustering
10.
Graphical Models
11.
Learning Theory and Expectation Maximization
12.
Introduction to Reinforcement Learning
We will assume that the students know programming for some of the assignments.If the students have done introdcutory courses on probability theory and linear algebra it would be helpful. We will review some of the basic topics in the first two weeks as well.
T. Hastie, R. Tibshirani, J. Friedman. The Elements of Statistical Learning, 2e, 2008.
Christopher Bishop. Pattern Recognition and Machine Learning. 2e.
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