CS112: Computer System Modeling Fundamentals
Lectures: Tuesdays & Thursdays, 4:00-5:50pm, Kinsey Pavilion 1200B
Discussions: Fridays, 4:00-5:50pm, Kinsey Pavilion 1200B

Recent Announcements

Course Overview

Don't be confused by the title! This course is designed to help students develop the mathematical reasoning skills necessary to solve problems that involve uncertainty. The foundational problem solving skills you will learn in this class are crucial for many exciting areas of computer science that inherently involve uncertainty, including artificial intelligence and machine learning, data mining, financial modeling, natural language processing, bioinformatics, web search, algorithm design, cryptography, system design, network analysis, and more. These skills will also help you analyze the uncertainty in your day-to-day life.

The first half of the course will cover the basics of probability, including probabilistic models, conditional probability, discrete and continuous random variables, expectation, mean and variance, the Central Limit Theorem, and the Law of Large Numbers. The second half of the course will focus on Markov chains and statistical inference.

Statistics 100A or 110A is required for this course. Although we will review all of the basics of probability in class, we will go through some of this material very quickly. If you are not familiar with basic concepts like random variables and expectation, the first half of the course will be more challenging and require extra effort from you.

Staff and Office Hours

Instructor: Prof. Jenn Wortman Vaughan
Office Hours: Wednesdays, 1:00-3:00, 4532H Boelter Hall
Contact: jenn at cs

TA: Ethan Schreiber
Office Hours: Mondays, 11:30-1:30, 2432 Boelter Hall
Contact: ethan at cs

Grader: Brian Geffon
Contact: briangeffon at gmail

Breakdown of Grades

Grades will be based on the following components:

UPDATE: If it is in your best interest, we will count the final exam for 45% of your final grade and the midterm for 15% instead of the standard 30% and 30%. Remember, the final will be a cumulative exam.

Schedule & Readings

The required textbook for this course is Introduction to Probability (2nd Edition) by Dimitri P. Bertsekas and John N. Tsitsiklis. We will cover Chapters 1-3, parts of Chapters 4 and 5, and parts of Chapters 7-9. Assigned readings will be posted here throughout the quarter. To get the most out of class, you should complete the required reading before each lecture.

Slides will be posted here after each lecture for your convenience, but reading the slides is not a good substitute for coming to class. In particular, the slides generally do not contain the details of the proofs and examples that we will go over in class.

Homework Assignments

Homework assignments will be posted here throughout the quarter.

Academic Honesty Policy

Collaboration on the homework assignments is encouraged! Discussing the problems with others can help you learn. Students are free to discuss the homework problems with anyone in the class under the following conditions:

  1. Each student must write down his or her solutions independently, and must understand the solutions he or she writes down. Talking over solutions is fine, but reading or copying another student's answers is not acceptable!
  2. Each student must write a list of all of his or her collaborators at the top of each assignment. This list should include anyone with whom the assignment was discussed.

These policies are described in the Academic Honesty Policy that must be signed by every student in the class. The Dean of Students also has a a guide to Academic Integrity.