Probability and Information Theory in Machine Learning
Covers probabilistic tools and information theory for machine learning, including classification, regression, and graphical models.
Summary
Historical reviews of John Gubner: Three tricky exams determine 60% of the grade, creating a needlessly stressful environment.
Grade history
↗letter grades
All recorded terms · compare terms & instructors
Prerequisites
Course map(MATH 320, 340, 341, 375, orM E/COMP SCI/E C E 532 or concur enroll) and (E C E 331,STAT/MATH 309, 431,STAT 311, 324, 424,MATH 531, or M E 424 prior to Fall 2026), grad/profsnl standing, or decl in Capstone Cert in Computer Sciences for Professionals
- MATH 320 · prior_or_concurrent
- MATH 340 · prior_or_concurrent
- MATH 341 · prior_or_concurrent
- MATH 375 · prior_or_concurrent
- COMPSCI/ECE/ME 532 · prior_or_concurrent
- ECE 331
- MATH/STAT 309
- MATH/STAT 431
- STAT 311
- STAT 324
- STAT 424
- MATH 531
- M E 424 prior to Fall 2026
This is a best-effort interpretation; check the catalog requirements above.
Prerequisite text tree
Professors
Fall 2026No instructors recorded for this selection.
Historical instructors & teaching patterns
Historical reviews of John Gubner: John Gubner is highly knowledgeable and curves well, often resulting in high grades. However, his lectures are difficult to follow, assuming prior knowledge, and the course is stressful due to logistics and tricky exams.
JOHN GUBNER is recorded teaching in Fall 2023. Recorded history may be incomplete and does not establish a future schedule.
Recorded history may be incomplete and does not establish a future schedule.
Calendar & sections
Fall 2026Schedule loads here as you scroll.
| Section | Mode | Enrolled / capacity | Waitlist |
|---|
Times are Central. Select a meeting for details; export includes recorded dates for the selected sections.
Meeting source records
No records available.
Student experience
the class
Historical reviews of John Gubner: John Gubner's course is highly stressful due to logistics and difficult lectures, though he is knowledgeable and curves well.
Recent recorded grades — Fall 2022: 3.62 GPA, 76.6% A/AB (n=47 letter grades); Fall 2023: 3.40 GPA, 61.7% A/AB (n=47 letter grades); Fall 2024: 3.96 GPA, 97.5% A/AB (n=40 letter grades).
difficulty & workload
Historical reviews of John Gubner: Three tricky exams determine 60% of the grade, creating a needlessly stressful environment.
Historical reviews of John Gubner: Lectures are hard to follow as if assuming prior knowledge, but the curve allows most students to get A or AB grades.
Topics
Skills
Grades
Latest available · Fall 2024— not enough history to project Fall 2026.
Grade distribution · % of letter grades
Grades over time
Through Fall 2024
More grade details Grade mix, volume & source data
Where this course fits relative to
Latest available grades · Fall 2024 · all course levels
GPA
Higher than % of other courses in this group.
Course GPAs · red marks this course’s range
letter grades
More recorded grades than % of other courses in this group.
Typical course in this group: letter grades.
About this comparison
1333 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.
Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.
Sources & history
Catalog & offerings
Descriptions, prerequisites, and recorded course offerings.
Catalog observation history
Observations at scan time; dates do not imply when a catalog change took effect.
Selected offering source records
No offering records for the selected term.
Raw records
[]
Student reviews
Original comments behind the course and instructor summaries.
Read original reviews
Grade history
Recorded grade distributions by term, section, and instructor.
Explore recorded gradesModel outputs & technical records
LLM outputs across runs
Full model traces
Recorded model configuration, reasoning, and tool conversations.
Model & dataset provenance
{
"model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
"model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
"task_version": "14",
"output_id": "050fc7ba8de8e4716f07d21683f659a360eb2fb31e766ed1a2b36adb09640f08",
"requirements_status": "needs_review",
"dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
"observed_at": "2026-09-07 15:55:43.033547+00:00"
}