Lecture Notes || SNS Courseware

Subject Details

Dept : ECE

Sem : 5

Regul : 2019

Faculty : M.Bhuvaneswari

phone : NIL

E-mail : bhuvanamohan.m@gmail.om

579

Page views

Files

Videos

R.Links

Lecture Notes

UNIT 1:

Definition of learning systems Goals and applications of machine learning

Types of Machine Learning Machine Learning Process

Testing Machine Learning Algorithms

Terminology-Weight Space

The Curse of Dimensionality

Testing Machine Learning Algorithms

UNIT 2:

Regression: Linear Regression

Parametric Models- Multivariate Regression Regression:

Classification: Bayesian Decision Theory parametric and non-parametric methods

Multivariate Classification Logistic Regression

K-Nearest Neighbor classifier

Decision Tree based methods for classification and Regression Ensemble methods

Classification: Bayesian Decision Theory parametric and non-parametric methods

UNIT 3:

Clustering-K-means clustering

Principal Component Analysis

Hierarchical Clustering

UNIT 4:

The Brain and The Neuron

Perceptron-Training the perceptron

Perceptron Learning Algorithm

Multilayer Perceptron

Back Propagation Dimensionality Reduction

UNIT 5:

Convolutional Networks

Recurrent Neural Networks

Bidirectional RNNs

Deep Recurrent Networks

Recursive Neural Networks

Lecture Notes