JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Course Objectives:

• To be able to formulate machine learning problems corresponding to different applications.
• To understand a range of machine learning algorithms along with their strengths and weaknesses.
• To understand the basic theory underlying machine learning.

UNIT - I

Introduction: An illustrative learning task, and a few approaches to it. What is known from algorithms? Theory, Experiment. Biology. Psychology. Overview of Machine learning, related areas and applications. How machines learn, Machine learning in practice, Managing and Understanding Data, R data structures Linear Regression, Multiple Regression, Logistic Regression, logistic functions, Case study using R - Predicting medical expenses using Linear Regression Concept Learning: Version spaces. Inductive Bias. Active queries. Mistake bound/ PAC model, basic results. Overview of issues regarding data sources, success criteria.

UNIT - II

Decision Tree Learning: - Minimum Description Length Principle. Occam’s razor. Learning with active queries Introduction to information theory, Decision Trees, Cross Validation and Over fitting Neural Network Learning: Perceptions and gradient descent back propagation, multilayer networks and back propagation.

UNIT - III

Sample Complexity and Over fitting: Errors in estimating means. Cross Validation and jackknifing VC dimension. Irrelevant features: Multiplicative rules for weight tuning. Support Vector Machines: functional and geometric margins, optimum margin classifier, constrained optimization, Lagrange multipliers, primal/dual problems, KKT conditions, dual of the optimum margin classifier, soft margins, and kernels. Bayesian Approaches: The basics Expectation Maximization. Bayes theorem, Naïve Bayes Classifier, Markov models, Hidden Markov Models, Case tool for Case study using R – filtering mobile phone spam with the Naive Bayes algorithm

UNIT - IV

Instance-based Techniques: Lazy vs. eager generalization. K nearest neighbor, case- based reasoning. Clustering and Unsupervised Learning: K-means clustering, Gaussian mixture density estimation, model selection Case study using R - finding teen market segments using k-means clustering

UNIT - V

Genetic Algorithms: Different search methods for induction - Explanation-based Learning: using prior knowledge to reduce sample complexity. Dimensionality reduction: feature selection, principal component analysis, linear discriminant analysis, factor analysis, independent component analysis, multidimensional scaling, manifold learning

TEXT BOOKS:

• 1. Tom Michel, Machine Learning, McGraw Hill, 1997
• 2. Brett Lantz, Machine Learning with R, 2/e, Kindle Edition, http://pdf.th7.cn/down/files/1602/Machine%20Learning%20with%20R,%202nd%20Edition.pdf
• 3. Trevor Has tie, Robert Tibshirani & Jerome Friedman. The Elements of Statically Learning, Springer Verlag, 2001

REFERENCE BOOKS:

• 1. Machine Learning Methods in the Environmental Sciences, Neural Networks, William W Hsieh, Cambridge Univ Press.
• 2. Richard o. Duda, Peter E. Hart and David G. Stork, pattern classification, John Wiley & Sons Inc.,2001 M. Tech- I Year – II Semester (Information Technology)