Syllabus (Elective): M.Tech

Pattern Recognition and Machine Learning

Code: EE 626 | L-T-P-C : 3-0-0

Introduction: Problem framing, feature selection, dimensionality reduction using PCA and other methods; Discriminative classifiers: LDA, Multi-layer perceptron, backpropagation, SVM; Unsupervised learning: Clustering, Vector Quantization, Kohonen Map, EM Algorithm; Generative models: Definition and characteristics, probabilistic graphical models, density estimation in learning; Combining classifiers: Advantages, boosting, hierarchical classifiers, and issues; Selected special topics such as manifold learning and case studies.

Texts:

1. 1. S. Marsland, Machine Learning: An Algorithmic Perspective, Chapman & Hall/CRC, 2009.
   2. R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, 2nd Edn., Wiley India, 2007.

References:

1. 1. C. . Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics), Springer, 2006.
   2. I. H. Witten, Data Mining: Practical Machine Learning Tools And Techniques, 2nd Edn., Elsevier India, 2008.