Lecture Notes || SNS Courseware

Subject Details

Dept : BIO

Sem : 5

Regul : R2019

Faculty : N.Jayashree

phone : NIL

E-mail : jayashree.n.ece@snsct.org

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Lecture Notes

UNIT 1:

Terminology-Weight Space

Learning Algorithms

Definition of learning systems Goals and applications of machine learning

Types of Machine Learning Machine Learning Process

Learning Algorithms

UNIT 2:

Parametric Models- Multivariate Regression

Classification: Bayesian Decision Theory parametric and non-parametric methods

K-Nearest Neighbor classifier

Decision Tree based methods for classification and Regression Ensemble methods

Regression: Linear Regression

UNIT 3:

Introduction

Principal Component Analysis

Clustering-K-means clustering

EM algorithm

Hierarchical Clustering

Probabilistic PCA

Introduction

UNIT 4:

The Brain and The Neuron

Back Propagation Dimensionality Reduction

Perceptron-Training the perceptron

Perceptron Learning Algorithm

Perceptron-Training the perceptron

Neural Networks

UNIT 5:

Convolutional Networks

Recurrent Neural Networks

Bidirectional RNNs

Applications Speech Recognition

Deep Recurrent Networks

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