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Unit - 1 Introduction

Unit 1

Introduction

1.1 Basic definitions

1.2 Linear Algebra

1.3 Statistical learning theory

1.4 Types of learning

1.5 Hypothesis space and Inductive bias

1.6 Evaluation and cross validation

1.8 Optimization

Unit - 2 Statistical Decision Theory

Unit 2

Statistical Decision Theory

2.1 Statistical Decision Theory

2.2 Bayesian Learning ML MAP Bayes estimates Conjugate priors

2.3 Linear Regression

2.4 Ridge Regression

2.5 Lasso

2.6 Principal Component Analysis

2.7 Partial Least Squares

Unit - 3 Linear Classification

Unit 3

Linear Classification

3.1 Linear Classification

3.2 Logistic Regression

3.3 Linear Discriminant Analysis

3.4 Quadratic Discriminant Analysis

3.5 Perceptron

3.6 Support Vector Machines Kernels

3.7 Artificial Neural Networks Back Propagation

3.8 Decision Trees

3.9 Bayes Optimal Classifier

3.10 Naive Bayes

Unit - 4 Hypothesis Testing

Unit 4

Hypothesis testing

4.1 Hypothesis testing

4.2 Ensemble Methods

4.3 Bagging Adaboost Gradient Boosting

4.4 Clustering

4.5 Kmeans

4.6 Kmedoids

4.7 Densitybased Hierarchical

4.8 Spectral

Unit - 5 Expectation Maximisation

Unit 5

Expectation Maximization

5.1 Expectation Maximization

5.2 GMMs

5.3 Learning theory Intro to Reinforcement Learning

5.4 Bayesian Networks

Download CSE Third Year syllabus pdf
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Other Subjects of third-year
Compiler design
Computer networks
Software engineering
Theory of computation
Artificial intelligence
Advance java programming
Database management systems
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