NPTEL : NOC:Probability for Computer Science (Computer Science and Engineering)

Co-ordinators : Prof. Nitin Saxena


Lecture 1 - Introductory examples

Lecture 2 - Examples and Course outline

Lecture 3 - Probability over discrete space

Lecture 4 - Inclusion-Exclusion principle

Lecture 5 - Probability over infinite space

Lecture 6 - Conditional probability, Partition formula

Lecture 7 - Independent events, Bayes theorem

Lecture 8 - Fallacies, Random variables

Lecture 9 - Expection

Lecture 10 - Conditional Expectation

Lecture 11 - Important Random Variables

Lecture 12 - Continuous Random Variables

Lecture 13 - Equality Checking, Poisson Distribution

Lecture 14 - Concentration Inequivalities, Variance

Lecture 15 - Weak Linearity of Variance, Law of Large Numbers

Lecture 16 - Chernoff's Bound. K-wise Independence

Lecture 17 - Union and Factorial Estimates

Lecture 18 - Stochastic Process: Markov Chains

Lecture 19 - Drunkard's walk, Evolution of Markov Chains

Lecture 20 - Stationary Distribution

Lecture 21 - Perron-Frobenius Theorem, Page Rank Algorithm

Lecture 22 - Page Rank Algorithm: Ergodicity

Lecture 23 - Cell Genetics

Lecture 24 - Random Sampling

Lecture 25 - Biased Coin Tosses, Hashing

Lecture 26 - Hashing, Introduction to Probabilistic Methods

Lecture 27 - Ramsey Numbers, Large Cuts in Graphcs

Lecture 28 - Sum Free Subsets, Discrepancy

Lecture 29 - Extremal Set Families

Lecture 30 - Super Concentrators

Lecture 31 - Streaming Algorithms - I

Lecture 32 - Streaming Algorithms - II