ECS 256 Syllabus, Fall 2022

Instructors

• Professor: Norm Matloff, matloff@cs.ucdavis.edu ; OHs M 1-3, W 2-3, 3053 Kemper; my bio
• TA: Jay Bendre, jdbendre@ucdavis.edu

Goals

To develop skills in modeling of Markov chains and other stochastic processes, and to better understand the modeling process itself.

Topical Coverage

See the Topic Outline and Reading List for a detailed account.

Prerequisites

A calculus-based probability course, like STA 131A or MAT 135A. Linear algebra.

This is definitely a math course. Thought we will indeed do some coding, this is not a "What function should I call?" course.

Class Format

I will lecture mainly from the readings, and will begin each lecture by saying something like "Please turn to page 88." I will then discuss the reading, explaining the "big picture," goals and so on.

I will generally not write on the board, nor give slide presentations. I will often present spur-of-the-moment examples (which will use the board), that arise from class discussion. I will also frequently make impromptu comments not in the readings.

I regard class discussion, often arising by a question raised by a student, to be one of the most important parts of a class.

Exams

• Four quizzes. The last, held on December 2 (last day of lecture), will be a group quiz, done collaboratively in your homework group.
• Open book, open notes.
• Exams are given through OMSI.. Though it is a math course, you will use R to express your answers.
• NOTE CAREFULLY that though the readings are the primary source, many exam questions will be based on class discussion, definitely including my impromptu comments.

Assignments

• Approximately 3-4 problem sets. Derivations, applications to real data.
• Term project, due on the day of the scheduled final exam. (We do not have an actual final.) Workload here will be comparable to about half a problem set.
• All assignments must be written up in LaTeX. Turn in your .tex file and resulting .pdf, as well as any .R and image files that you use. Images must be generated from R, using either base-R, ggplot2 or lattice. Please NOTE CAREFULLY: All work must be reproducible by me; in some cases, I may even try running your code in somewhat different settings, to better understand what you did. Solutions turned in to TA on CSIF, via handin.

Groups

This is a major, major issue.

You will rely quite heavily on your homework groups for problem sets, the group quiz, and the term project. Get to know them well!

• Group size must be 3 or 4. If you wish to form your own group, let the TA know during Week 1 of class. Otherwise the TA will assign you to a group.
• Group work should be collaborative, not "You do Problem 1, I'll do Problem 2 etc."
• Within a group, there may be differing levels of math or coding skill, so not everyone might contribute equally. But everyone must do their part.

  In working with your teammates on the problem sets, and later on the term project, you ARE part of a team, and you are expected to do your best to contribute to the team. You cannot say, for instance, "Oh, I just want an S grade in this course, so I will do less." This is unethical, irresponsible behavior toward your teammates.

  If, in interactive grading, it becomes clear that you contributed little or nothing to the team effort, YOU WILL NOT GET CREDIT FOR THE ASSIGNMENT. If this happens on multiple occasions, you will get you a D or F for the course, regardless of your quiz grades etc. If a member of your group is not participating, notify me IMMEDIATELY.

Blog

It is required that you read the course blog at least once per day. All course announcements will be made there. Material in blog posts may also be subject to exam questions.

Grading

Please note that the grading of the problem sets will be INTERACTIVE. Your group will sign up for a time to come to my office for grading, and during the grading session I will ask you questions, INDIVIDUALLY. Where does this equation come from? What is this code doing? If the problem prompt had been such-and-such, how would your solution change? I may also ask you questions on the course material itself.

Since I do ask questions individually, it is possible that not everyone gets the same grade within a given group on a given problem set.

I will grade the exams using the OMSI tool.

Tentatively, I expect to give 50% weight on the exams, and 50% on the assignments (problem sets, term project).`

Final Comments

I enjoy teaching very much, and look forward to working with you. If you have any constructive suggestions or concerns during the quarter, please do not hesitate to let me know.

I've always liked the field of Markov chains. I hope you find them captivating too.