Lecture Notes || 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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Lecture Notes

UNIT 1:

Machine Learning – perspective -Issues

Machine Learning algorithms

Turning data into Probabilities, and Statistics for Machine Learning

Examples of Machine Learning Applications

UNIT 2:

Shrinkage Methods – Derived Input Directions

Linear Models for Classification- Discriminant Analysis

Logistic Regression

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

Case Study: Handwriting Recognition

Back Propagation Algorithms

Neural Network Representation – Problems – Perceptron

UNIT 4:

Introduction - Association Rules

Apriori Algorithm - Clustering- K-means

EM Algorithm- Mixtures of Gaussians

Self-organizing Map - Principal Components

Curves and Surfaces – Independent Component Analysis

UNIT 5:

Introduction - Single State Case

Elements of Reinforcement Learning

Model Based Learning

Temporal Difference Learning–Generalization

Partially Observable States

Case Study: Healthcare Prediction

Lecture Notes