15-259/559 Probability and Computing (PnC)
12 Units, SPRING 2026

It is at the core of artificial intelligence and machine learning, which require decision making under uncertainty. It is integral to CS theory, where probabilistic analysis and randomization form the basis of 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.

TEXTBOOK FOR CLASS

Discrete & Continuous Probability Probability on events. Discrete and continuous random variables. Conditioning and Bayes. Variance and 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 simulation. Statistics Estimators for mean and variance. Maximum likelihood estimation (MLE). MAP estimation. Bayesian statistical inference. Confidence intervals.

If you like this class, consider taking the follow-on class with the same textbook: 15-359 PnC II. That class covers Chapters 18-27: Chernoff bounds, Hoeffding Bounds, Balls-and-Bins Problems, Hashing, Randomized Algorithms, Discrete-Time Markov Chains, Ergodicity Theory.

CLASS/RECITATION TIMES:

• Lectures: TUESDAY and THURSDAY 12:30 pm - 1:50 pm, TEP 1403

• Recitations:

  • A: FRIDAY 9:00 am - 9:50 am, PH A19A, TA: TBD
  • B: FRIDAY 1:00 pm - 1:50 pm, WEH 8427, TA: George Liu
  • C: FRIDAY 2:00 pm - 2:50 pm, WEH 5328, TA: Rohan Jain
  • D: FRIDAY 1:00 pm - 1:50 pm, WEH 6403, TA: Rishi Prabhu
  • F: FRIDAY 3:00 pm - 3:50 pm, WEH 5312, TA: Pranav Sangwan

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