The aim of this IST is to teach young mathematics and computer science teachers working in colleges and universities the mathematics behind many of the Algorithms in Computer Science. Starting with basic algorithms on graphs like minimum spanning trees, matchings and flows,  we hope to take participants to a point where they can be taught sophisticated algorithms which get used in machine learning like principal component analysis, clustering, random forests and page ranking. We will see the use of discrete mathematics in the design and analysis of algorithms on graphs and later see how linear algebra gets used in in convex optimization algorithms and many machine learning algorithms. Along the way we will introduce participants to the theory of NP completeness, linear programming and convex programming. Time permitting we may do more sophisticated algorithms for machine learning.

Syllabus to be covered in terms of modules of 6 lectures each

References:

1. Introduction to Algorithms, Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein, MIT Press/Prentice Hall India

2. Algorithm Design, Jon Kleinberg and Eva Tardos, Pearson

3. Foundations of Data Science, Arvim Blum, John Hopcroft, and Ravi Kannan

4. Lecture notes, tutorials, and slides from the internet, as appropriate

Names of the tutors / course associate with their affiliation and status:

1. K Sandesh Kamath, Research Scholar, CMI

2. Muthuvel Murugan, Research Scholar, CMI

3. Rajit Datta, Research Scholar, CMI

4. Nada Pulath, IIT Madras

Tentative time-table: