Week 1: Course Introduction and Setup
Week 1 introduces the course structure and the tools we will use throughout the semester. It also checks the mathematical, statistical, and computing foundations needed for later topics.
Guiding question: How can GitHub and an AI coding agent help me organize, check, and submit my work without replacing my own understanding?
Learning goals
By the end of this week, you should be able to:
- explain how lectures, homework, discussion questions, Tuesday sessions, quizzes, and the final project fit together;
- create and verify the
stat432-fall2026GitHub repository; - confirm that at least one AI coding agent is available for the course repository;
- prepare and submit a discussion question in the required format; and
- identify prerequisite topics that need further review.
Lecture notes
Course Introduction and Structure (PDF)
Tuesday lecture. Review the course design, weekly workflow, grading, discussion sessions, quizzes, final project, homework requirements, and appropriate use of AI tools.
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Thursday lecture, Part 1. Create
stat432-fall2026, make one coding agent available, and learn how it can assist with setting up a local copy of the repository. Discussion Questions: Procedures and Rules
Thursday lecture, Part 2. Review the submission rules, learn how the optional
stat432-fall2026-studentskill can check technical format, prepare and submit your first discussion question, and learn how Tuesday discussions and quizzes operate.
Homework 01
This homework contains six required problems that check the mathematical, statistical, and computing prerequisite knowledge used in this course. All homework assignments are evaluated as Complete or Incomplete.
Place homework-01.pdf or homework-01.html in the homework/week-01/ folder of your stat432-fall2026 GitHub repository by Sunday, August 30, 2026, at 11:59 p.m. Central Time. The report must be either a PDF or a self-contained HTML file and must not exceed 10 pages. The final file version shown on GitHub is treated as the submitted version, and its GitHub timestamp determines whether it is on time.
Discussion Session on Tuesday, September 1, 2026
Submit discussion/week-01-question.md by Sunday, August 30, 2026, at 11:59 p.m. Central Time. This is the first required weekly discussion question and counts toward the Weekly GitHub Discussion Questions component, worth 10% of the course grade.
The final version shown on GitHub is the submitted version. If its GitHub timestamp is after the deadline, the submission receives 0 points. No late-submission exceptions are allowed.
The portable stat432-fall2026-student skill records the repository structure and submission allowlist, filenames, and technical format checks. It does not review substantive content. For discussion-question metadata, only your Illinois NetID is required as the author identifier. Your real or full name and GitHub username are optional.
Install the STAT 432 student submission skill from
https://raw.githubusercontent.com/teazrq/stat432-fall2026-student/v1.0.1/SKILL.md
in the local skills folder supported by this coding agent. Save it as
stat432-fall2026-student/SKILL.md. Use it only to set up the required
GitHub folders or to check and submit my discussion-question, homework,
or final-project files. Check technical format only. Do not review or
revise substantive content.Review and proofread each finished question before you commit and push it to GitHub.
The guided first discussion session is Tuesday, September 1, 2026. Open Popping when the instructor asks you to join.
See the Discussion tab for login information.
Tell the instructor during class if you cannot sign in. There is no quiz during this session.
Submit the GitHub account you will use for this course through the GitHub account information form.
Discussion Question Topics
The first discussion is intentionally flexible. Your question may concern any of the following topics:
Prerequisite concepts. Probability and mathematical statistics from STAT 410, linear regression from STAT 425, or linear algebra from MATH 415, especially how these ideas may appear in statistical learning.
AI agents and related concepts. For example, what distinguishes an agent from a chatbot, what a skill does, what an agent harness is, or how tools and instructions affect an agent’s behavior.
Git and GitHub. For example, the relationship between Git and GitHub, the purpose of commits, the difference between committing and pushing, or how version control supports reproducible work.
Checking AI-generated work. How can mathematical reasoning, code, simulations, documentation, or counterexamples be used to detect a plausible but incorrect AI answer?
Reproducible statistical computing. For example, why random seeds, recorded package versions, readable code, and saved outputs matter when working in R or Python.
Your question does not need to be advanced. It should, however, invite explanation, comparison, reasoning, or a small demonstration. Questions that only ask for help with an installation error should be posted on the Canvas Discussion board.
Week 1 Check List
Save the exact error message or take a screenshot. Post the error or issue on the Canvas Discussion board so that other students can benefit from the answer. You may also email rqzhu@illinois.edu, with chenyuz9@illinois.edu copied. Use STAT 432 Fall 2026 as the subject line.