Course Syllabus

CS177: Applications of Probability in Computer Science

University of California, Irvine, Fall 2017

Prof. Erik Sudderth

Probability and statistics play a key role in real-world applications of computer science. Examples include the modeling of text and web data, speech recognition, robotics, network traffic and system reliability modeling, probabilistic analysis of algorithms and graphs, machine learning and data mining, cryptography, and more. In this course, students will expand their knowledge of probabilistic models and methods, and apply them to diverse computational problems. The mathematical topics we will study include conditioning and Bayes' rule, joint distributions of discrete and continuous random variables, independence and conditional independence, covariance and bivariate normal distributions, rare events, limit theorems, and discrete-time Markov processes.

Course Materials

• Piazza will be used for all course announcements, discussions, and questions.  All enrolled students should sign up, watch for important announcements, and post questions (anonymously if you prefer) about course content.
• Lecture Calendar, including readings and slides
• Homework Assignments and Exams
• Homework Policies and Resources, including the Collaboration and Academic Honesty Policy
• Python Resources for homework assignments
• Detailed syllabus (pdf)

Course Information

• Textbook:  Introduction to Probability, second edition.  Dimitri P. Bertsekas & John N. Tsitsiklis, Athena Scientific, 2008.  Available at the UCI Bookstore.
• Lectures:  Tuesdays and Thursdays from 2:00-3:20pm, Rowland Hall 104.
• Instructor:  Prof. Erik Sudderth.  Office hours Wednesdays from 2:00-3:30pm, DBH 4028.
• Teaching Assistant:  John (Gabriel) Hope.  Office hours Mondays and Wednesdays from 4:00-5:00pm, ICS 424A.
• Reader:  Geng Ji.  Office hours Tuesdays and Thursdays from 1:00-2:00pm, ICS 424A.

Course Prerequisites

An introductory course in probability and statistics (STATS 67). Courses in calculus (MATH 2B), linear algebra (MATH 3A or I&C SCI 6N), and discrete mathematics (I&C SCI 6B, I&C SCI 6D). Basic Python programming experience required for homework assignments.

Exams and Course Grades

Overall course grades will be assigned as follows: 40% homeworks, 25% midterm exam, 35% final exam. The midterm exam will be given during the normal lecture time on Tuesday, November 7. The final exam will be given on Thursday, December 14 from 1:30pm-3:30pm. Exams must be taken at these times. Exceptions are granted only for medical or family emergencies.

Exams will include questions similar to the non-programming portions of the homework assignments. Electronic devices are not allowed. You are not allowed to bring notes or other reference materials, but we will provide a reference page with useful mathematical formulas.