Lecture Notes || SNS Courseware

Subject Details

Dept : CSE

Sem : 5

Regul : R2019

Faculty : Devi

phone : NIL

E-mail : devi.g.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

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

Subset Selection

UNIT 3:

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

Neural Networks – Fitting Neural Network - Bayesian Neural Net

Neural Network Representation – Problems – Perceptron

Multilayer Networks

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

Model Based Learning

Temporal Difference Learning–Generalization

Lecture Notes