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Jharkhand University of Technology, Jharkhand, Computer Engineering Semester 7, Machine Learning Syllabus
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Unit - 1 What is Machine learning
Unit 1
Machine learning
1.1 What is Machine learning
1.2 Basic principal
1.3 Utility of ML Well defined learning system
1.4 Designing learning system
1.5 Challenges in ML
1.6 Application of ML
Unit - 2 Linear Regression
Unit 2
Linear Regression
2.1 Linear Regression with one variable and multiple variables
2.2 Gradient Descent
2.3 Classification Logistic Regression Over fitting Regularization Support Vector Machines
2.4 Decision Trees and issue in decision tree
2.5 Bayesian Learning – Bayes Theorem
2.6 Concept Learning
2.7 Bayes Optimal Classifier
2.8 Naïve Bayes Classifier
2.9 Bayesian Belief Networks
2.10 EM Algorithm
Unit - 3 Clustering
Unit 3
Clustering
3.1 Clustering Kmeans Hierarchical etc.
3.2 Dimensionality reduction
3.3 Principal Component Analysis
3.4 Anomaly detection
3.5 Feasibility of learning
3.6 Reinforcement learning
Unit - 4 Artificial Neural Networks
Unit 4
Artificial Neural Networks
4.1 Artificial Neural Networks
4.2 Artificial Perceptron
4.3 Gradient Descent and The Delta Rule
4.4 Adaline
4.5 Multilayer Networks
4.6 Backpropagation Rule backpropagation Algorithm Convergence
Unit - 5 Evolutionary algorithm
Unit 5
Evolutionary algorithm
5.1 Evolutionary algorithm
5.2 Genetic Algorithms – An Illustrative Example
5.3 Hypothesis Space Search
5.4 Genetic Programming
5.5 Swarm intelligence algorithm
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Other Subjects of Semester-1
Artificial intelligence
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