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MLSIG: Machine Learning Special Interest Group 

Department of Computer Science and AutomationIndian Institute of Science

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Background Courses

The CSA Department at IISc offers several background courses that will help you build a strong foundation towards studying machine

learning. If you are a PhD or Masters' student interested in working in machine learning, it is extremely important that you build a

strong background in the following subjects as soon as possible on joining IISc:

E0 232: Probability and Statistics

E0 219: Linear Algebra and Applications

E0 225: Design and Analysis of AlgorithmsE0 230: Computational Methods of Optimization

Introductory / Intermediate Courses

There are several introductory and intermediate level courses, offered both within CSA and in other departments, that will teach you

the fundamental tools and techniques in machine learning and related fields:

E0 270: Machine Learning

E1 213: Pattern Recognition and Neural Networks

E0 268: Data Mining

E0 233: Information Theory, Inference and Learning Algorithms

E0 290: Mathematical Foundations for Modern Computing

Advanced Courses

Finally, a variety of more advanced courses are offered at various times that will help you connect the material learned in previous

courses with topics of current research interest:

E0 370: Statistical Learning Theory

E0 269: Probabilistic Graphical Models

E0 371: Topics in Machine Learning

E1 313: Topics in Pattern Recognition

E1 395: Topics in Stochastic Control and Reinforcement Learning

E0 352: Topics in System Research: Learning for Computer Systems

E0 330: Convex Optimization

Courses on Other Topics

In addition to the courses listed above, there are a variety of other courses in CSA and other departments at IISc that you may wish to

explore. For example, several of our students take courses in the Mathematics, EE, and ECE departments to further strengthen their 

mathematical and statistical backgrounds; in particular, if you are a PhD student working in machine learning or learning theory, you

may find some of the following courses beneficial:

MA 221: Analysis I

MA 222: Analysis II

MA 223: Functional Analysis

MA 368: Topics in Probability and Stochastic Processes

MA 369: Random Matrix Theory

E2 201: Information TheoryE2 202: Random Processes

E2 212: Matrix Theory

Some students take courses in biology or in other fields in which they want to apply machine learning techniques. Within computer 

science, subjects such as algorithms, complexity, graph theory, and game theory all provide useful techniques and have fascinating

SIG: Machine Learning Special Interest Group | CSA, IISc http://drona.csa.iisc.ernet.in/~mlcenter/courses.html

2 02-06-2012 01:21

7/31/2019 MLSIG_ Machine Learning Special Interest Group _ CSA, IISc

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interfaces with machine learning; subjects such as information retrieval, natural language processing, computer vision, communication

networks, and computer systems all provide natural problems where machine learning techniques are applied. Finding new bridges

between machine learning and other disciplines is up to your imagination!

SIG: Machine Learning Special Interest Group | CSA, IISc http://drona.csa.iisc.ernet.in/~mlcenter/courses.html

2 02-06-2012 01:21