CS112: Modeling Uncertainty in Information Systems

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Course Overview

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 sample spaces and probability, conditional probability, discrete and continuous random variables, expectation, mean and variance, and the Law of Large Numbers, with some applications to information theory. The second half of the course will focus on inference and Markov chains.

STAT 100A 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.

Meeting Times

Lectures: Mondays & Wednesdays, 2:00-3:50pm, Boelter 2444

Discussion Sections:
Fridays, 2:00-3:50pm, Boelter 2444 (Section 1A)
Fridays, 4:00-5:50pm, Boelter 5436 (Section 1B)

Regular attendance at both lectures and sections is required. There will be frequent in-class exercises that count for 15% of your grade. Please attend the section for which you are enrolled.

Staff and Office Hours

Instructor: Prof. Jenn Wortman Vaughan (jenn at cs)
Office Hours: Thursdays 11am-noon and by appointment, 4532H Boelter Hall

TA: Jacob Mathew (jacobgmathew at gmail)
Office Hours: Tuesdays 11am-1pm, 2432 Boelter Hall

Graders: Ding Zhao (zhaoding at ucla) and Jake Stothard (stothardj at gmail)

Breakdown of Grades

Grades will be based on the following components:

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 also 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.

This schedule is tentative and may shift a little as we get deeper into the material.

Homework Assignments

Homework assignments will be posted here throughout the quarter.

Piazza Discussion Board

We will be using Piazza for class discussion. Rather than emailing questions to the teaching staff, you are encouraged to post your questions on Piazza. Find our class page here.

Good uses of Piazza include:

The course Academic Honesty Policy must be followed on Piazza and at all times. Do not post or request homework solutions! And please be polite.

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.