Lecture Notes || SNS Courseware

Subject Details

Dept : CSE

Sem : 5

Regul : 2019

Faculty : Poornima devi L

phone : NIL

E-mail : poornima.l.cse@snsct.org

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Lecture Notes

UNIT 1:

Machine Learning – perspective -Issues

Examples of Machine Learning Applications

Types of Machine Learning –Machine Learning process- preliminaries, testing

Turning data into Probabilities, and Statistics for Machine Learning

UNIT 2:

Introduction - Linear Models for Regression – Linear Regression Models and Least Squares

Shrinkage Methods – Derived Input Directions

UNIT 3:

Logistic Regression

Boosting and Additive Trees – Boosting Trees – Regularization – Interpretation – Illustrations

Neural Network Representation – Problems – Perceptron

Back Propagation Algorithms

Multilayer Networks

UNIT 4:

Introduction - Association Rules

Apriori Algorithm - Clustering- K-means

Self-organizing Map - Principal Components

EM Algorithm- Mixtures of Gaussians

UNIT 5:

Introduction - Single State Case

Model Based Learning

Partially Observable States

Elements of Reinforcement Learning

Lecture Notes