Probability and statistics have become indispensable tools in computer science. Probabilistic methods and statistical reasoning play major roles in machine learning, cryptography, network security, communication protocols, web search engines, robotics, program verification, and more. This course introduces the basic concepts of probability and statistics, focusing on topics that are most useful in computer science applications. Topics include: modeling and solution in sample space, random variables, simple random processes and their probability distributions, Markov processes, limit theorems, and basic elements of statistical inference.

This course emphasizes both mathematical rigor and computing applications. Topics are similar to Statistical Inference I (APMA 1650) with more of a focus on modern applications and formal proof.

Fall 2021: Lectures will be held during K hr: Tuesdays and Thursdays 2:30-3:50pm in CIT 368. Recitation sessions will be arranged according to a survey sent out during the first week of class. TA hours will be held in a hybrid format in order to accomodate students and course staff, via Zoom and at TBD locations and times. These times will be posted on the course calendar.

Problem Sets We will distribute weekly problem sets throughout the semester. You are expected to write up and submit solutions to these problem sets on your own. You are encouraged to discuss problems with your classmates as a method of brainstorming and determining various approaches to solving problems. However, you must ensure that your write-up of the solutions are authored entirely on your own.

Please review the syllabus for additional information.

If you have any questions about the course, please email the HTA Connor.