Learning Objectives
The objective of the course is to equip the student to recognize and solve modern machine learning problems those arise in practical applications.
Learning Outcome
At the end of this course students will be able to implement and use some of the stateoftheart techniques of machine learning. They will be able to compare pros and cons of various methods in terms of using them to solve some real world problems. Students will be able to analyse the complexity of an optimization problem and its solution.
Course Content
Hierarchical Bayesian models: generative models, Topic models, Bayesian nonparametrics [12 lectures]
Deep learning, artificial neural networks [7 lectures]
Distributed Computing : parallel programming and Mapreduce framework, High Performance Computing. [4 lectures]
Sequential learning: Bayesian sequential learning, stochastic gradient descent [3 lectures]
Kernel Learning, multi view spatiotemporal learning, structure prediction [12 lectures]
Privacyaware learning [4 lectures]
Textbooks

Ian Goodfellow, Yoshua Bengio and Aaron Courville. Deep Learning. The MIT Press, 2016. ISBN13: 9780262035613.

Dimitri P. Bertsekas and J. Tsitsiklis. Parallel and Distributed Computation, Athena Scientific. 2015. ISBN 1886529159.

Christopher Bishop. Pattern Recognition and Machine Learning. Springer. 2006. ISBN13 9780387310732.
References

Bernhard Schölkopf and Alexander J. Smola. Learning with Kernel. The MIT Press 2001 ISBN: 9780262253437

Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, and Jonathan Eckstein. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine Learning 3, no. 1 (2011): 1122.

Neal Parikh and Stephen Boyd. Proximal algorithms. Foundations and Trends® in Optimization 1, no. 3 (2014): 127239.

Yu Nesterov . Introductory lectures on convex programming volume I: Basic course. Lecture notes (1998).
Past Offerings
 Offered in JulyDec, 2018 by Sahely, Mrinal
Course Metadata
Item  Details 

Course Title  Topics in Machine Learning 
Course Code  CS5001 
Course Credits  3003 
Course Category  ERC 
Approved on  Senate of IIT Palakkad 