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
Name of the Speaker with affiliation, who will cover each module of 6 lectures. 
No. of Lectures 
Detailed Syllabus 
Gee Varghese Philip, CMI 
3 
Sorting, DFS, BFS, minimum spanning tree algorithms, matchings in bipartite graphs, analysis of algorithms 
Meghana Nasre, IIT Madras Prajakta Nimbhorkar, CMI 
8 
Network flows, reduction, NP completeness, TSP, vertex cover, integer programming 
K V Subrahmanyam, CMI 
4 
Algorithms for rank, solving linear equations, linear programming, applications of LP,gradient descent, formulating convex programs, simple update algorithms. 
Madhavan Mukund, Sourish, K V Subrahmanyam, CMI 
9 
Best fit subspaces, PCA, clustering, page ranking, SVD, Support vector machines, random forests. 
References:

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

Algorithm Design, Jon Kleinberg and Eva Tardos, Pearson

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

Lecture notes, tutorials, and slides from the internet, as appropriate
Names of the tutors / course associate with their affiliation and status:

K Sandesh Kamath, Research Scholar, CMI

Muthuvel Murugan, Research Scholar, CMI

Rajit Datta, Research Scholar, CMI

Nada Pulath, IIT Madras
Tentative timetable:
Day 
Date 
Lecture 1 (9.30–11.00) 
Tea (11.00–11.30) 
Lecture 2 (11.30–1.00) 
Lunch (1.00–2.30) 
Tutorial 1 (2.30–3.30) 
Tea (3.304.00) 
Tutorial 2 (4.005.00) 
Tea & Snacks 5.005.30 


name of the speaker 

name of the speaker 

name of the speaker/tutor 

Name of the speaker/tutor) 

Mon 
18.6.2018 
Philip 

Prajakta 

TBD 

TBD 

Tues 
19.6.2018 
Philip 

Prajakta 

TBD 

TBD 

Wed 
20.6.2018 
Philip 

Meghana 





Thu 
21.6.2018 
Meghana 

Prajakta 





Fri 
22.6.2018 
Meghana 

Prajakta 





Sat 
23.6.2018 
Meghana 

KV 





Sunday : Off 

Mon 
25.6.2018 
KV 

Sourish 





Tue 
26.6.2018 
KV 

Sourish 





Wed 
27.6.2018 
KV 

Madhavan 





Thur 
28.6.2018 
KV 

Madhavan 





Fri 
29.6.2018 
KV 

Madhavan 





Sat 
30.6.2018 
KV 

Madhavan 




