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
UNIT 3:
Boosting and Additive Trees – Boosting Trees – Regularization – Interpretation – Illustrations
Boosting and Additive Trees – Boosting Trees – Regularization – Interpretation – Illustrations
Boosting and Additive Trees – Boosting Trees – Regularization – Interpretation – Illustrations
Neural Networks – Fitting Neural Network - Bayesian Neural Net
Neural Networks – Fitting Neural Network - Bayesian Neural Net
Neural Network Representation – Problems – Perceptron
Back Propagation Algorithms
UNIT 4:
Curves and Surfaces – Independent Component Analysis
Introduction - Association Rules
Apriori Algorithm - Clustering- K-means
EM Algorithm- Mixtures of Gaussians
Self-organizing Map - Principal Components
UNIT 5:
Introduction - Single State Case
Elements of Reinforcement Learning
Temporal Difference Learning–Generalization
Temporal Difference Learning–Generalization