Connected successfully
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
Introduction - Linear Models for Regression – Linear Regression Models and Least Squares – Subset Selection – Shrinkage Methods – Derived Input Directions - Linear Models for ClassificationDiscriminant Analysis – Logistic Regression – Separating Hyper planes - Neural Networks. Case Study: Handwriting Recognition.
Introduction - Association Rules – Apriori Algorithm - Clustering- K-means – EM Algorithm- Mixtures of Gaussians - Self-organizing Map - Principal Components, Curves and Surfaces – Independent Component Analysis. Case Study: Weather prediction.
Introduction - Single State Case - Elements of Reinforcement Learning – Model Based Learning - Temporal Difference Learning – Generalization - Partially Observable States. Case Study: Healthcare Prediction
Knowledge representation techniques - problem solving - search techniques - game playing - knowledge and logic - learning methods.
Reference Book:
1 Pattern Recognition and Machine Learning (Information Science and Statistics) reprint of the original 1st ed. 2006 Edition by Christopher M. Bishop 2 Artificial Intelligence: A Modern Approach (Pearson Series in Artificial Intelligence) 4th Edition by Stuart Russell, Peter Norvig,2008 3 Mitchell T, “Machine Learningâ€, McGraw-Hill, 1997
Text Book:
1 Saikat Dutt, Subramanian Chandramouli, Amit Kumar Das “Machine Learningâ€, First Edition, Pearson Paperback, 2018