Concepts in Machine Learning- CST 383 KTU Minor Notes- Dr Binu V P

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Syllabus

Previous Year Question Papers-Concepts in Machine Learning CST 383

Module 1: 

Overview of  Machine Learning

Bayesian Formulation

Maximum a Posteriori (MAP), a Bayesian method/Maximum Likelihood Estimation (MLE),

Module 2: Supervised Learning

Supervised Learning,Regression,

Perceptron

Naive Bayes Classifier

Decision Trees-ID3

Module 3:

Introduction to Neural Networks

Neural Networks and activation functions

Multi Layer Neural Networks, back propagation

Back-propagation Example

Activation Functions

Implementation of a two layer XOR network with  sigmoid activation

Application of Neural Networks

Support Vector Machines ( SVM)

Module 4:Unsupervised Learning

Similarity Measures

Representative-based Clustering(K-means and Expectation-Maximization Algorithms)

Hierarchical Clustering-Agglomerative Clustering ( AHC)

Dimensionality Reduction-Principal Component Analysis ( PCA)

Factor Analysis

Linear Discriminant Analysis ( LDA)

Module 5:

Classification Assessment

Cross validation

Face Detection

Comments

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  2. Rag17 June 2024 at 23:02

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Syllabus Concepts in Machine Learning- CST 383 KTU

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