Connected successfully Syllabus || SNS Courseware
Subject Details
Dept     : CSE
Sem      : 5
Regul    : R2019
Faculty : Ms.S.R.Janani
phone  : NIL
E-mail  : jananiselvaraj.mit@gmail.com
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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 Classification- Discriminant Analysis – Logistic Regression – Separating Hyper planes.

UNIT
3
DEEP LEARNING

Boosting and Additive Trees – Boosting Trees – Regularization – Interpretation – Illustrations -Neural Networks – Fitting Neural Network - Bayesian Neural Net - Neural Network Representation – Problems – Perceptron – Multilayer Networks and Back Propagation Algorithms. Case Study: Handwriting Recognition.

UNIT
4
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
5
REINFORCEMENT LEARNING

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

Reference Book:

Tom M. Mitchell, “Machine Learning”, McGraw-Hill Education (India) Private Limited, 2013. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, “An Introduction to Statistical Learning: with Applications in R”, Springer; First Edition 2013. P. Flach, ―Machine Learning: The art and science of algorithms that make sense of data, Cambridge University Press, 2012.

Text Book:

1 AlpaydinEthem, “Introduction to Machine Learning”, MIT Press, Second Edition, 2010. 2 Trevor Hastie, Robert Tibshirani, Jerome Friedman, “The Elements of Statistical Learning: Data Mining, Inference, and Prediction”, Springer; Second Edition, 2009.