Connected successfully Syllabus || SNS Courseware
Subject Details
Dept     : CSE
Sem      : 7
Regul    : 2019
Faculty : vanitha G
phone  : 948621790
E-mail  : vanitha.g.cse@snsct.org
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Syllabus

UNIT
1
INTRODUCTION

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
SUPERVISED LEARNING

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.

UNIT
3
UNSUPERVISED LEARNING

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.

UNIT
4
REINFORCEMENT LEARNING

Introduction - Single State Case - Elements of Reinforcement Learning – Model Based Learning - Temporal Difference Learning – Generalization - Partially Observable States. Case Study: Healthcare Prediction

UNIT
5
AUTOMATION

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 Learning”, McGraw-Hill, 1997

Text Book:

1 Saikat Dutt, Subramanian Chandramouli, Amit Kumar Das “Machine Learning”, First Edition, Pearson Paperback, 2018