Spring 2027

Probability for Actuaries

Probability for Actuaries develops fundamental mathematical tools for quantitatively assessing risk, emphasizing applications to problems in actuarial science.

offering recorded1 credits
Recorded instructors · Spring 2027 Margie Rosenberg3.8/5Gordon Enderle

Summary

1 / 5

Past student reviews · Gee Lee

One student felt the pace was too fast and assumed prior knowledge, leading to confusion. The course is a review for Exam P, often covering topics before concurrent probability courses do.

What you’ll be able to do

  • Mathematically represent common single variable discrete and continuous probability functions that are relevant in actuarial applications such as Poisson, binomial, negative binomial, and geometric, uniform, exponential, and normal.

  • Calculate measures related to the characteristics of single variable probability functions such as moments, mean, variance, median, mode, percentiles, etc., and interpret them in an actuarial context.

  • Derive joint, marginal and conditional probabilities and densities from multivariate distributions and calculate covariance and correlation coefficients, and interpret them in an actuarial context.

Source: UW Guide

Grade history

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average GPA
letter grades
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All recorded terms · compare terms & instructors

Prerequisites

Course map

(STAT/​MATH 309,MATH 331,STAT 333, 340, or concurrent enrollment), or declared in undergraduate Business Exchange program. Not open to graduate/professional students.

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027
Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 4.25/5 from 4 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

RMP profile ↗ · All captured review dates; profile matched by name.

No course-specific feedback yet.

Historical instructors & teaching patterns

DOUGLAS BUJAKOWSKI is recorded teaching in Fall 2012, Fall 2013, Spring 2014, Fall 2014, Spring 2015. Recorded history may be incomplete and does not establish a future schedule.

GEE LEE is recorded teaching in Fall 2015, Spring 2016, Fall 2016. Recorded history may be incomplete and does not establish a future schedule.

PAUL JOHNSON is recorded teaching in Fall 2021, Spring 2022, Fall 2022, Spring 2023, Fall 2025, Spring 2026. 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

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 420
LEC 002Classroom Instruction0 / 420

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records

Student experience

the class

Past student reviews · Paul Johnson

The class was described as easy and engaging, with an instructor who clearly knows the material without overwhelming students. He was noted as caring and accommodating.

Recent recorded grades — Spring 2019: 3.91 GPA, 95.7% A/AB (n=46 letter grades); Fall 2019: 3.91 GPA, 100.0% A/AB (n=11 letter grades); Spring 2020: 3.93 GPA, 97.6% A/AB (n=42 letter grades).

difficulty & workload

Past student reviews · Gee Lee

One student felt the pace was too fast and assumed prior knowledge, leading to confusion. The course is a review for Exam P, often covering topics before concurrent probability courses do.

Past student reviews · Doug Bujakowski

A past student found it easy to get an A but recommended studying as you go. They had to cram hard for the Exam P despite passing, after skating through the class.

Topics

  • Single variable discrete and continuous probability functions including Poisson, binomial, and normal distributions.
  • Multivariate distributions, joint, marginal, and conditional probabilities, and covariance.
  • Quantitative risk assessment in actuarial science.

Skills

  • Representing discrete and continuous probability functions like Poisson, binomial, and normal distributions.
  • Calculating and interpreting statistical measures such as moments, mean, variance, and percentiles.
  • Deriving joint, marginal, and conditional probabilities from multivariate distributions.

Grades

Latest available · Spring 2020— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2020

More grade details Grade mix, volume & source data

Sources & history

UW–Madison

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
ACTSCI 300 · Spring 2027

Probability for Actuaries

Recorded 2026-10-29
Raw records
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    "run_id": "20261029T213040-0fa85c20",
    "semester": "1274",
    "observed_at": "2026-10-29 21:30:40.248904+00:00",
    "offering_id": "1274:242:021357",
    "course_id": "ACTSCI 300",
    "course_uid": "course_d5c3b831cc97e1d5bef0f5ff",
    "term_id": "1274",
    "source_course_id": "021357",
    "source_subject_id": "242",
    "title": "Probability for Actuaries",
    "credits_min": 1,
    "credits_max": 1,
    "typically_offered": "Fall, Spring"
  }
]
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Student reviews

Original comments behind the course and instructor summaries.

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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Model & dataset provenance
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  "output_id": "d778645da296f1547029e270767a796b8fb603248f4b0c6308562996e1f65484",
  "requirements_status": "needs_review",
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  "observed_at": "2026-10-29 21:30:40.248904+00:00"
}