Large Deviation Bounds

Monte Carlo method

Sample Complexity

Randomized Algorithms

Packet Routing

Martingales

Rademacher complexity

Probabilistic Method

Hashing

Markov Chains

CSCI 1550/2450 Probabilistic Methods in Computer Science Brown University Spring 2022 This analytical course dives into the mathematical underpinnings of computing successes like machine learning and cryptography, emphasizing the role of probability, randomness, and statistics. Students will explore mathematical models, theorems, and proofs. Practical implementations are not covered, focusing instead on the theories driving computational probabilities. CSCI 1550/2450 is a course on the mathematics that motivates, formulates, and explains many of the great successes of computing, including statistical machine learning, Monte Carlo methods, and modern cryptography. Probability, randomness, and statistics play a key role in these and almost any other modern computer science application. This course introduces the novel mathematical and computation methods that were developed at the interplay of probability and computing. The course focuses on mathematical models, theorems and proofs, and leaves implementation and experiments to other courses. - CSCI 1550/2540 is a theory/analytical course - analysis, theorems, no implementations.
- The course covers modern mathematics at the interface of probability theory and computation
- Formulates, and explains many of the great successes of computing, such as machine learning, cryptography, modern finance, computational biology, etc.
- This course focuses on tools, not particular applications. CS145 or equivalent (first three chapters in the course textbook). ## Textbook

The textbook for the course is *[Probability and Computing Randomization and Probabilistic Techniques in Algorithms and Data Analysis, 2nd Edition](https://www.amazon.com/Probability-Computing-Randomization-Probabilistic-Techniques/dp/110715488X/ref=dp_ob_title_bk)* by [Michael Mitzenmacher](http://www.eecs.harvard.edu/%7Emichaelm/) and [Eli Upfal](/people/eli).

[Errata for the 1st Printing](http://www.eecs.harvard.edu/%7Emichaelm/MUErrataPrint1.pdf), [Errata for the 2nd Printing](http://www.eecs.harvard.edu/%7Emichaelm/MUErrataPrint2.pdf)

CSCI 1550/2450 Probabilistic Methods in Computer Science

Mathematical Foundations

Large Deviation Bounds

1 courses cover this concept

CSCI 1550/2450 Probabilistic Methods in Computer Science