Other university all, Computer Engineering , Machine Learning Syllabus

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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

1.7 Optimization

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

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

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

5.1 Expectation Maximization

5.2 GMMs

5.3 Learning theory Intro to Reinforcement Learning

5.4 Bayesian Networks

Download CSE Level 3 syllabus pdf
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Other Subjects of level-3
Cyber security
Computer graphics
Computer algorithm
Internet of things
System programming
Software engineering
Artificial intelligence
Computer graphics and multimedia techniques
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