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Bihar Engineering University, Bihar, Computer Engineering Semester 6, Machine Learning Syllabus
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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
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Other Subjects of Semester-2
Compiler design
Computer networks
Advance java programming
Speech and audio processing
Cryptography and network security
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