Syllabus
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Course Code: ST-403 & ST-404 Course Name: (iii) Machine Learning |
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| MODULE NO / UNIT | COURSE SYLLABUS CONTENTS OF MODULE | NOTES |
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| 1 | What is Machine Learning, Why Machine Learning is Required, Relation to Artificial Intelligence, Current Applications & Future of Machine Learning in Various Industries, Basic Process of any Machine Leaning System, Terminologies used in Machine Learning, Evaluation Metrics in Machine Learning, Machine Learning Categories , Supervised Learning, Unsupervised learning, Reinforcement Learning. | |
| 2 | Understanding of Supervised Learning with example, Vapnik-Chervonenkis (VC) Dimension, PAC Learning, Regression, Model Selection and Generalization, Dimensions of a Supervised Machine Learning Algorithm, Bayesian Decision Theory, Parametric Methods : Maximum Likelihood Estimation, Regression, Model Selection Procedure, Multivariate Methods: Multivariate Data, Multivariate Classification, Tuning Complexity, Multivariate Regression; Support Vector Machines, Random Forest. | |
| 3 | Non Parametric Methods: Histogram Estimator, Kernal Estimator, k Nearest Neighbor Estimator, Non Parametric Classification, Condensed Nearest Neighbor, Non Parametric Regression – Smoothing Models, How to Choose Smoothing Parameter. Decision Trees :Univariate Trees, Classification Trees, Regression Trees, Pruning, Rule Extraction from Trees, Learning Rules from Data, Multivariate Trees. |
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| 4 | Unsupervised Machine Learning: k-Means Clustering, Expectation Maximization Algorithm, Supervised Learning after Clustering, Hierarchical Clustering, Choosing the number of Clusters. Neural Network(NN) : Introduction, Important Concepts in NN, The Perceptron, Training a Perceptron, Learning Boolean Functions, Multilayer Perceptron, MLP as a Universal Approximator, Backpropogation Algorithm, Training Procedures, Tuning the Network Size, Bayesian View of Learning, Dimensionality Reduction, Learning Time. |
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