MLSIG: Machine Learning Special Interest Group
 
Indian 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:

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:

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:

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 Theory
  • E2 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 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!