Syllabus
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Course Code: MCA-20-44 (ii) Course Name: Machine Learning |
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| MODULE NO / UNIT | COURSE SYLLABUS CONTENTS OF MODULE | NOTES |
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| 1 | Machine Learning: Introduction to Machine Learning, Overview of Machine Learning, Key Terminology and
task of ML, Applications of ML; Supervised Learning: Classification, Decision Tree Representation- Appropriate problem for Decision Learning, Decision Tree Algorithm, Hyperspace Search in Decision Tree; |
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| 2 | Naive Bayes- Bayes Theorem, Classifying with Bayes Decision Theory , Conditional Probability, Bayesian
Belief Network; Regression: Linear Regression- Predicting numerical value, Finding best fit line with linear regression, Regression Tree- Using CART for regression. |
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| 3 | Logistic Regression - Classification with Logistic Regression and the Sigmoid Function; Clustering: Learning from unclassified data –Introduction to clustering, K-Mean Clustering, ExpectationMaximization Algorithm(EM algorithm), Hierarchical Clustering, Supervised Learning after clustering. |
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| 4 | Dimensionality reduction- Dimensionality reduction techniques, Principal component analysis, Anomaly
Detection, Recommender Systems; SVM, Reinforcement Learning. |
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