15-259/559 Probability and Computing (PnC), 12 Units FALL 2024

THE COOLEST of PROBABILITY CLASSES: Where probability meets chocolate

Probability theory has become indispensable in computer science.

It is at the core of machine learning, where one often needs to make decisions under stochastic uncertainty. It is also integral to computer science theory, where probabilistic analysis and ideas based on randomization appear in many algorithms. It is a central part of performance modeling in computer networks and systems, where probability is used to predict delays, schedule resources, and provision capacity.

This course gives an introduction to probability as it is used in computer science theory and practice, drawing on applications and current research developments as motivation and context.

TEXTBOOK FOR CLASS

The course notes for the class have now been published by Cambridge University Press 2024. We will be covering Chapters 1-17 of this textbook.

Discrete & Continuous Probability Probability on Events. Discrete and Continuous Random Variables. Conditioning and Bayes. Higher Moments. Laplace transforms and z-transforms. Gaussians and Central Limit Theorem. Tails and Stochastic dominance. Systems Modeling Heavy-tailed distributions. Poisson processes. Simulation of random variables. Event-driven simulations Statistics Estimators for mean and variance. Maximum likelihood estimation (MLE). MAP estimation. Bayesian statistical inference. Confidence intervals.

If you like this class, please consider taking the follow-on class with the same textbook: 15-359 Probability and Computing II. That class covers the following topics:
Chernoff bounds, Hoeffding Bounds, Balls-and-Bins Problems, Hashing, Randomized Algorithms, Discrete-Time Markov Chains, Ergodicity Theory.

CLASS/RECITATION TIMES:

PROFESSORS:

Mor Harchol-Balter harchol@cs.cmu.edu OH: Wed 5:30 pm - 7:00 pm, GHC 7207

Feras Saad fsaad@cs.cmu.edu OH: tbd, GHC 9225

TAs:

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