The course is an introduction to probability theory and statistics, with an emphasis on solving problems in computer science and engineering. Probability and statistics is an important foundation for computer science fields such as machine learning, artificial intelligence, computer graphics, randomized algorithms, image processing, and scientific simulations. Topics in probability include discrete and continuous random variables, probability distributions, sums and functions of random variables, the law of large numbers, and the central limit theorem. Topics in statistics include sample mean and variance, estimating distributions, correlation, regression, and hypothesis testing. Beyond the fundamentals, this course will also focus on modern computational methods such as simulation and the bootstrap. Students will learn statistical computing using the freely available R statistics software.
Hours: 3:40-5:00 PM Tue/Thu
Location: WEB L104
Office hours: Thursday 10 AM - Noon (MEB 3470)
There are no formal pre-requisites for the class, but a knowledge of basic discrete mathematics (from CS 2100), familiarity with proof techniques such as induction will be assumed.
The main textbook for the course is A Modern Introduction to Probability and Statistics: Understanding Why and How, by F.M. Dekking, C. Kraaikamp, H.P. Lopuhaa, L.E. Meester. The book can be downloaded for free from Springer (via the university) from this page.
R programming. The course will also introduce you to basic programming in R. Here are two very good references. (There are plenty more online, including some Youtube videos. Feel free to search for more, and discuss on Piazza.)
See the course canvas page for the schedule and a list of topics. The page will be updated by the TAs on a regular basis, and will contain pointers to the lecture slides, the textbook and potential additional reading.
Additional course logistics such as the grading rubric and other policies are explained in this document. **Please read it carefully!**
We will be using Piazza for course discussions. This is also the preferred mode of interaction with the instructor and the TAs.
We have five TAs for the course: