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  • 12:56 AM, Tuesday, 28 Jan 2020


Course Postgraduate
Semester Sem. II
Subject Code ESG624
Subject Title Pattern Recognition and Machine Learning

Syllabus

 

Kernel Methods:  Introduction to metric space, vector space, normed space, inner product space; RKHS; Learning theory;  SVM for classification & regression; implementation techniques of SVM;   kernel ridge regression;  kernel density estimation;  kernel PCA; kernel online learning.Random forest, Genetic algorithms, ant colony optimization

 

Spectral Clustering; model based clustering, Expectation Maximization; Independent Component Analysis; Hidden Markhov models;  Factor Analysis; introduction to Graphical models & Sampling Methods.

 

Basic concepts of machine learning, inductive learning, decision tree learning, semi-supervised learning, ensemble learning, clustering, artificial neural networks, support vector machines, bayesian learning, deep learning, Convolution neural network, accuracy assessment


Text Books

1.Machine Learning for Spatial Environmental Data: Theory, Applications, and Software (Environmental Sciences: Environmental Engineering) 1st Edition Mikhail Kanevski,VadimTimonin,Alexi Pozdnukhov

2. Deep learning by Ian Goodfellow, Yoshua Bengio,Aaron Courville, MIT Press, 2016.

3 Neural Networks and Learning Machines (3rd Ed) by Simon Haykin, McMaster University, Canada,2008

References

1. Pattern Recogonition and Machine learning Christopher M Bishop 2006

2.    Machine Learning, Tom Mitchell, McGraw Hill, 1997