# CS5691: Pattern recognition and Machine learning

## Course information

**When**: Jan-May 2020**Lectures**: Slot J**Where**: CS25**Teaching Assistants**: TBA

## Course Content

Review of probability

Bayes decision theory and Bayes classifer

Maximum likelihood and Bayesian parameter estimation

Statistical learning theory

PAC learning, empirical risk minimization, uniform convergence and VC-dimension

Linear models

Linear least-squares regression, logistic regression, regularized least squares, bias-variance tradeoff

Dimensionality reduction

Singular value decomposition, principal component analysis

Support vector machines and kernel methods

Online learning

Prediction with expert advice, Perceptron and Winnow algorithms

Mixture densities and EM algorithm

K-means clustering

Multilayer neural networks

Feedforward networks, backpropagation

The schedule of lectures from the 2019 run of this course is available here

## Grading

Quiz-I and II: 15% each

Final exam: 30%

Mini-quizzes: 10% (Best 2 out of 3)

Programming Assignment(PA)-I and II: 10% each

Programming Contest: 10%

## Important Dates

Mini-quizzes: Feb 3,

~~Mar 9~~,~~Apr 13~~Quiz-I: Feb 19, Quiz-II:

~~Mar 18~~Final:

~~May 2~~PA-I: Available on Feb 20, submit by

~~Mar 8~~PA-II: Available on Mar 20, submit by

~~Apr 8~~Contest:

~~Apr 25~~

## Textbooks

Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar,

*Foundations of Machine Learning*, MIT Press, 2019Christopher M. Bishop,

*Pattern Recognition and Machine Learning*, Springer, 2006.Richard O. Duda, Peter E. Hart and David G. Stork,

*Pattern Classification*, John Wiley, 2001

Additional References

Shai Shalev-Shwartz and Shai Ben-David,

*Understanding Machine Learning*, Cambridge Univ. Press, 2014Simon Haykin.

*Neural networks and learning machines*, Pearson Education, 2009.Bernhard Scholkopf, Alexander J. Smola,

*Learning with Kernels*, MIT press, 2002.Michael Kearns and Umesh Vazirani,

*An Introduction to Computational Learning Theory*, MIT press, 1994.