Spring 2027

Applied Machine Learning for Behavioral Data Science

An introduction to computational machine learning approaches for behavioral sciences, covering R programming, model implementation, and evaluation.

offering recorded3 credits
Recorded instructors · Spring 2027 John Curtin4.0/5

Summary

1 / 3

Recent recorded grades — Spring 2024: 3.57 GPA, 70.4% A/AB (n=27 letter grades); Spring 2025: 3.64 GPA, 75.9% A/AB (n=29 letter grades); Spring 2026: 3.64 GPA, 75.9% A/AB (n=29 letter grades).

What you’ll be able to do

  • develop and refine best practices for data wrangling, general programming, and analysis in R.

  • distinguish among a variety of learning settings: supervised learning vs. unsupervised learning, regression vs. classification

  • implement a broad toolbox of well-supported machine-learning methods: decision trees, nearest neighbor, general and generalized linear models, penalized models including ridge, lasso, and elastic-nets, neural nets, support vector machines

  • develop expertise with common feature extraction techniques for quantitative and categorical predictors

  • use natural language processing approaches to extract meaningful features from text data

  • know how to characterize how well their regression and classification models perform and they will employ appropriate methodology for evaluating their: cross validation, ROC and PR curves, hypothesis testing

  • learn to apply their skills to common learning problems in psychology and behavioral sciences more generally

Source: UW Guide

Grade history

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

Prerequisites

Course map

PSYCH 610 and graduate/professional standing

  • take all
PSYCH 752 used by

“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
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Professors

Spring 2027
Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 4.86/5 from 7 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.

Recent recorded grades — Spring 2024: 3.57 GPA, 70.4% A/AB (n=27 letter grades); Spring 2025: 3.64 GPA, 75.9% A/AB (n=29 letter grades); Spring 2026: 3.64 GPA, 75.9% A/AB (n=29 letter grades). Includes jointly taught sections.

Historical instructors & teaching patterns

JOHN J. CURTIN is recorded teaching in Spring 2023, Spring 2024, Spring 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 / 320

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

Meeting source records
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Student experience

the class

Recent recorded grades — Spring 2024: 3.57 GPA, 70.4% A/AB (n=27 letter grades); Spring 2025: 3.64 GPA, 75.9% A/AB (n=29 letter grades); Spring 2026: 3.64 GPA, 75.9% A/AB (n=29 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Machine learning methods including decision trees, linear models, penalized models, neural nets, and SVMs
  • Feature extraction techniques for quantitative and categorical predictors
  • Natural language processing for text data

Skills

  • Data wrangling, general programming, and analysis in R
  • Distinguishing supervised/unsupervised learning and regression/classification
  • Implementing machine learning methods like decision trees, SVMs, and neural nets
  • Using NLP to extract features from text data
  • Evaluating model performance using cross-validation, ROC/PR curves, and hypothesis testing

Grades

Latest available · Spring 2026— 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 2026

More grade details Grade mix, volume & source data

Not enough comparable courses for Spring 2027 in UW–Madison.

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.

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Selected offering source records
PSYCH 752 · Spring 2027

Applied Machine Learning for Behavioral Data Science

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:820:025911",
    "course_id": "PSYCH 752",
    "course_uid": "course_054fb4f0d08ea29f91467540",
    "term_id": "1274",
    "source_course_id": "025911",
    "source_subject_id": "820",
    "title": "Applied Machine Learning for Behavioral Data Science",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
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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.

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Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "376ac4218376f6d1476b1f871333414d3e745d36e1338f43b0d94ccc2b09edab",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}