Chennai Mathematical Institute

Schools, Workshops and Conferences


NCM IST Mathematics for Computer Science

June 18–30, 2018

NCM webpage for IST Mathematics for Computer Science (2018)

NCM IST June 2018 Group Photo, 28 Jun 2018 NCM IST June 2018 Group Photo, 29 Jun 2018

Lectures

  • Lecture 1, 18 June, 2018 (Lecture material), (Slides)

    Binary search, selection sort, insertion sort, merge sort

  • Lecture 2, 18 June, 2018 (Lecture material), (Slides)

    Quicksort, graphs and graph representations

  • Lecture 3, 19 June, 2018 (Lecture material), (Slides)

    Breadth first search, depth first search

  • Lecture 4, 19 June, 2018 (Lecture material), (Slides)

    Shortest paths (Dijkstra, Bellman-Ford, Floyd-Warshall), minimum cost spanning trees (Prim, Kruskal)

  • Lecture 5, 20 June, 2018

    Network flows, Ford-Fulkerson algorithm

  • Lecture 6, 20 June, 2018

    Network flows, Max flow-Min cut theorem

  • Lecture 7, 21 June, 2018

    Max flow to solve matchings, vertex cover in bipartite graphs, König's theorem

  • Lecture 8, 21 June, 2018

    Introduction to classes P, NP. NP completeness, NP hardness, Independent set, vertex cover, clique, reductions

  • Lecture 9, 22 June, 2018

    Scheduling problem, Make span, 2 approximation for make span

  • Lecture 10, 22 June, 2018

    TSP – Inapproximability of TSP, 2 approximation for metric TSP, reductions and approximations

  • Lecture 11, 23 June, 2018

    Linear inequalities, duality, lower bounds from dual problem

  • Lecture 12, 23 June, 2018

    Vector spaces, linear equations, Gaussian elimination, LUP decomposition

  • Lecture 13, 25 June, 2018

    Linear regression - an LP formulation of linear regression. Separating sets of points by a hyperplane - an LP formulation, Farkas's lemma. Complexity implications of Farkas's lemma.

  • Lecture 14, 25 June, 2018

    Sparse solutions to linear equations, LP formulation for minimizing l1 norm, sufficient conditions for sparse solutions, Largest ball in a convex region - an LP formulation.

  • Lecture 15, 26 June, 2018

    Regression via higher dimensional curves - a matrix formulation and analytical solutions, gradient descent, convex functions and convex domains, gradient descent analysis for Lipschitz convex functions.

  • Lecture 16, 26 June, 2018 (Lecture material)

    Introduction to Machine Learning (ML): supervised and unsupervised learning, examples

  • Lecture 17, 27 June, 2018 (Lecture material), (Slides)

    ML: Decision trees, information gain via entropy, handling continuous attributes, evaluating classifiers, precision and recall

  • Lecture 18, 27 June, 2018

    Multiplicative weight updates

  • Lecture 19, 28 June, 2018

    Ellipsoid algorithm for LP

  • Lecture 20, 28 June, 2018 (Lecture material), (Slides)

    ML: overfitting, ensemble models, frequent itemsets, a priori algorithm

  • Lecture 21, 29 June, 2018

    Naive Bayes classification, Singular Value Decomposition

  • Lecture 22, 29 June, 2018 (Lecture material)

    Expectation maximization, Latent Dirichlet Analysis

Tutorials

Lecture Notes

  • Lecture notes by Pallav D. Shah, P. D. Patel Institute of Applied Sciences, Charotar University of Science and Technology




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