Course

Module I

Introduction to Machine learning: Supervised learning, Unsupervised learning, The five tribes in Machine Learning, linear classification, perceptron update rule, Perceptron convergence, generalization, Maximum margin classification, Classification errors, regularization, logistic regression, Linear regression, estimator bias and variance, active learning

Module II

Non-linear predictions, kernels, Kernel regression, kernels, Support vector machine (SVM) and kernels, kernel optimization.

Module III

Model selection, Model selection criteria, Description length, feature selection, Combining classifiers, boosting, Bagging, margin, and complexity, Margin and generalization (EM) algorithm, EM, regularization, clustering, Clustering, Spectral clustering, Markov models, Hidden Markov models (HMMs), Bayesian networks, Learning Bayesian networks, Probabilistic inference, Current problems in machine learning.

Course Objectives

• To introduce mathematical methods for design of machine learning algorithms.
• To provide an overview of cluster analysis process and cluster quality evaluation techniques.
• To enable design and performance evaluation of classifiers for typical classification problems.
• To enable design of frequent itemset mining system for typical solve market-basket analysis problems.

Course Outcomes

• CO1: Able to generate, analyze and interpret data summaries.
• CO2: Able to carry out analysis on machine learning algorithms.
• CO3: Able to design and implement classifiers for machine learning applications.
• CO4: Able to design and implement frequent itemset mining systems.

CO – PO Mapping

Textbook(s)

• Bishop, Christopher. Neural Networks for Pattern Recognition. New York, NY: Oxford University Press, 1995

Reference(s)

• Duda, Richard, Peter Hart, and David Stork, “Pattern Classification” Second Edition, New York, NY: WileyInterscience, 2000.
• Hastie, T., R. Tibshirani, and J. H. Friedman, “The Elements of Statistical Learning: Data Mining, Inference and Prediction”, New York, Springer, 2001.
• MacKay, David, “Information Theory, Inference, and Learning Algorithms”, Cambridge University Press, 2003.

Evaluation Pattern