Syllabus
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Course Code: MCA-20-44 Course Name: Elective-V - (ii) 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, and 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, Expectation-Maximization 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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