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Subject Details

Dept : ECE

Sem : 5

Regul : 2019

Faculty : A.Sakira Parveen

phone : NIL

E-mail : sakiraparveen.a.ece@snsct.org

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Lecture Notes

UNIT 1:

Types of Machine Learning

Definition of learning systems Goals and applications of machine learning

Machine Learning Process

Testing Machine Learning Algorithms

The Curse of Dimensionality

Terminology-Weight Space

UNIT 2:

Regression: Linear Regression,Parametric Models

Multivariate Regression

Classification: Bayesian Decision Theory parametric and non-parametric methods

Multivariate Classification- Logistic Regression

K-Nearest Neighbor classifier

Decision Tree based methods for classification & Regression Ensemble methods

UNIT 3:

Introduction & Clustering

K-means clustering

EM algorithm

Principal Component Analysis

Hierarchical Clustering

Probabilistic PCA

UNIT 4:

The Brain and The Neuron

Neural Networks

Multilayer Perceptron

Back Propagation -Dimensionality Reduction

Perceptron-Training the perceptron

Perceptron Learning Algorithm

UNIT 5:

Convolutional Networks

Recurrent Neural Networks

Bidirectional RNNs

Deep Recurrent Networks

Recursive Neural Networks

Applications- Speech Recognition

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