JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

UNIT - I

Supervised Learning (Regression/Classification) Basic methods: Distance-based methods, Nearest-Neighbours, Decision Trees, Naive Bayes Linear models: Linear Regression, Logistic Regression, Generalized Linear Models Support Vector Machines, Nonlinearity and Kernel Methods Beyond Binary Classification: Multi-class/Structured Outputs, Ranking

UNIT - II

Unsupervised Learning Clustering: K-means/Kernel K-means Dimensionality Reduction: PCA and kernel PCA Matrix Factorization and Matrix Completion Generative Models (mixture models and latent factor models)

UNIT - III

Evaluating Machine Learning algorithms and Model Selection, Introduction to Statistical Learning Theory, Ensemble Methods (Boosting, Bagging, Random Forests)

UNIT - IV

Biological foundations to intelligent Systems: Artificial Neural Networks. Single layer and Multilayer Feed Forward NN, LMS and Back Propagation. Algorithm, Feedback networks and Radial Basis Function Networks

UNIT - V

Fuzzy Logic, Knowledge Representation and Inference Mechanism, Defuzzification Methods Fuzzy Neural Networks and some algorithms to learn the parameters of the network like GA

TEXTBOOKS:

• 1. Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012
• 2. Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning, Springer 2009 (freely available online)
• 3. Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2007.
• 4. J M Zurada , “An Introduction to ANN”,Jaico Publishing House
• 5. Simon Haykins, “Neural Networks”, Prentice Hall R19 M. TECH. ES & VLSI DESIGN/VLSI & ES M.TECH.- II YEAR- I SEMESTER EMBEDDED SYSTEMS & VLSI DESIGN/VLSI & EMBEDDED SYSTEMS