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With the advent of machine learning, data mining, and many other modern applications of computer science, we are increasingly seeing the influence of probability theory on computer science. This course is aimed at providing a brief introduction to probability theory to CS students so that they can grasp recent CS trends more easily.
 

Week

Topics

1.

A brief axiomatic introduction to discrete probability theory – Karger’s Mincut

2.


Random Variables – Quicksort

3.


Markov’s and Chebyshev’s Inequalities – Randomized Median

4.


Chernoff Bounds – Parameter Estimation & Quicksort Revisited

A course on design and analysis of algorithms is a required prerequisite.


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