UNIT 1:
Machine Learning – perspective -Issues
Examples of Machine Learning Applications
Types of Machine Learning –Machine Learning process- preliminaries, testing
Machine Learning algorithms
Turning data into Probabilities, and Statistics for Machine Learning
Probability theory -Bayesian Decision Theory.
UNIT 2:
Introduction - Linear Models for Regression – Linear Regression Models and Least Squares
Shrinkage Methods – Derived Input Directions
Linear Models for Classification- Discriminant Analysis
UNIT 3:
Boosting and Additive Trees – Boosting Trees – Regularization – Interpretation – Illustrations
Neural Networks – Fitting Neural Network - Bayesian Neural Net
Neural Network Representation – Problems – Perceptron
Back Propagation Algorithms
Case Study: Handwriting Recognition