| Information | Data |
|---|---|
| Instructor | Professor Marek Rychlik |
| Office | Mathematics 605 |
| Telephone | 1-520-621-6865 |
| rychlik@arizona.edu | |
| Instructor Homepage/Web Server | http://alamos.math.arizona.edu |
| Course Homepage | http://alamos.math.arizona.edu/math466 |
| Course Homepage (Mirror) | http://marekrychlik.com/math466 |
| Personnel | Day of the Week | Hour | Room | Comment |
|---|---|---|---|---|
| Marek Rychlik | Monday | 5:30pm-6:30pm | Math 589B Zoom Link | Regular office hours |
| Marek Rychlik | Wednesday | 5:00pm-6:00pm | Math 589B Zoom Link | Regular office hours |
| Marek Rychlik | Friday | 10:00am-11:00am | Math 589B Zoom Link | Regular office hours |
Office hours by appointment are welcome. Please contact me by e-mail first, so that I can activate a Zoom link for the meeting.
The course format is that of a conventional lecture, with in-class discussion and additional web-delivered content. All lectures will be recorded and available on Zoom and Panopto.
Written homework will be assigned regularly and graded using Gradescope.
In addition, the course incorporates required programming assignments. Numerical experimentation is essential to understanding and using the course subject matter. The assignments will be graded by an autograder implemented in Gradescope.
This course is an introduction to the theory of stochastics. The student will gain understanding of the following fundamental concepts:
In this course you are welcome and expected to use generative artificial intelligence/large language model tools, e.g. ChatGPT, Dall-e, Bard, Perplexity. Using these tools aligns with the course learning goals such as developing writing and programming skills, and ability to effectively use available information. Be aware that many AI companies collect information; do not enter confidential information as part of a prompt. LLMs may make up or hallucinate information. These tools may reflect misconceptions and biases of the data they were trained on and the human-written prompts used to steer them. You are responsible for checking facts, finding reliable sources for, and making a careful, critical examination of any work that you submit. Your use of AI tools or content must be acknowledged or cited. If you do not acknowledge or cite your use of an AI tool, what you submit will be considered a form of cheating or plagiarism. Please use the following guidelines for acknowledging/citing generative AI in your assignments:
Students are expected to be present for all exams. If a verifiable emergency arises which prevents you from taking an in-class exam at the regularly scheduled time, the instructor must be notified as soon as possible, and in any case, prior to the next regularly scheduled class. Make-up exams and quizzes will be administered only at the discretion of the instructor and only under extreme circumstances. If a student is allowed to make up a missed exam, (s)he must take it at a mutually arranged time. No further opportunities will be extended. Failure to contact your instructor as stated above or inability to produce sufficient evidence of a real emergency will result in a grade of zero on the exam. Other remedies, such as adjusting credit for other exams, may be considered.
Introduction to probability theory. Hoel, Paul G., Stone, Charles J., Port, Sidney C.
Midterm examinations are planned to be administered during the class time. The final examination follows the University final exam schedule.
If, due to unforseen circumstances, they cannot be held in person, they are held on Zoom using the "gallery view" mode.The exam papers for not in-person tests will be distributed on-line by D2L and collected electronically using D2L "dropbox" feature.
| Exam or Assignment | Date | Grade contribution |
|---|---|---|
| Midterm 1 | TBD | 20% |
| Midterm 2 | TBD | 20% |
| Final Examination | December 17 (Thursday), 8:00am - 10:00am | 30% |
| Homework | See D2L | 30% |
Written homework consists of approximately twelve assignments equally contributing to the grade, each worth 30/12 = 2.5% of the grade. The assignments are posted and collected via Gradescope, which is cloud-based software for semi-automatic grading. Things to keep in mind:
Written homework is assigned regularly throughout the semester, for a total of approximately 80 problems. Two types of homework will be assigned:
Using Gradescope for grading differs from other grading systems. Mainly, it uses AI to allow the instructor to accurately grade a larger number of problems than it would be possible otherwise. Some grading is completely automated (e.g., solutions to problems with a numerical answer). More comples answers may be grouped automatically by using Machine Learning, OCR and image analysis. However, it is possible to completely confuse the system by improperly structuring the submitted document. Therefore, please read the instructions below carefully and re-visit them as needed. Note that Gradescope supports automatic regrade requests which you can use if all fails.
The solutions must be structured in such a way that Gradescope can read them and that its 'AI' can interpret them. Your homework must be submitted as a PDF document, even if you use scanner or phone to capture images. Two typical workflows will be as follows:
The class will have small programming assignments. It is expected that you will be using software to gain insights into the assigned problems and subject matter. The programming assignments must be submitted in formats supported by Gradescope and the instructor. The number of programming languages will be limited two two or three. R will be supported and it is encouraged that you use it as it is most compatible with the course content.
For illustrating some aspects of the course, I will be using these programs (easy to download and free to use):
The final examination is scheduled for: December 17 (Thursday), 8:00am - 10:00am.
The time, date and general exam rules are set by the University and can be found at these links:
The student in the class normally receives a letter grade A, B, C, D or E.
The cut-offs for the grades are:
| Grade | % Range |
|---|---|
| A | 90%+ |
| B | 80-90% |
| C | 70-80% |
| D | 60-70% |
| E | 0-60% |
Normally, individual tests and assignments will not be "curved". However, grade cut-offs may be lowered at the end of the semester (but not raised!) to reflect the difficulty of the assignments and other factors that may cause abnormal grade distribution.
The grade will be computed by D2L and the partial grade will be updated automatically by the system as soon as the individual grades are recorded.
General UA policy regarding grades and grading systems is available at https://catalog.arizona.edu/policy-type/grade-policiesFor a list of emergency procedures for all types of incidents, please visit the website of the Critical Incident Response Team (CIRT):
Our goal in this classroom is that learning experiences be as accessible as possible. If you anticipate or experience physical or academic barriers based on disability, please let me know immediately so that we can discuss options. You are also welcome to contact Disability Resources (520-621-3268) to establish reasonable accommodations. For additional information on Disability Resources and reasonable accommodations, please visit http://drc.arizona.edu/ .
If you have reasonable accommodations, please plan to meet with me by appointment or during office hours to discuss accommodations and how my course requirements and activities may impact your ability to fully participate. Please be aware that the accessible table and chairs in this room should remain available for students who find that standard classroom seating is not usable. Code of Academic Integrity Required language: Students are encouraged to share intellectual views and discuss freely the principles and applications of course materials. However, graded work/exercises must be the product of independent effort unless otherwise instructed. Students are expected to adhere to the UA Code of Academic Integrity as described in the UA General Catalog. See: http://deanofstudents.arizona.edu/academic-integrity/students/academic-integrity http://deanofstudents.arizona.edu/codeofacademicintegrity .
Fall 2026 - Undergraduate Regular Academic Session
Date
Event
August 24, 2026
Classes Begin
September 7, 2026
Labor Day - No Classes
November 11, 2026
Veterans Day - No Classes
November 26-29, 2026
Thanksgiving Recess - No Classes
December 9, 2026
Last Day of Classes and Laboratory Sessions
December 10, 2026
Reading Day - No Classes or Finals
December 11-17, 2026
Final Examinations
Official dates are maintained by the University of Arizona
Registrar.
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