Spring 2027

Introduction to Machine Learning and Statistical Pattern Classification

Introduction to Machine Learning and Statistical Pattern Classification covering classification, regression, clustering, dimensionality reduction, and model evaluation using Python.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · John Gillett

The workload was described as manageable compared to programming courses, though exam problems required careful review and some critical thinking.

What you’ll be able to do

  • Understand the different subfields of machine learning, such as supervised and unsupervised learning and being familiar with essential algorithms from each subfield.

  • Identify whether machine learning is appropriate for solving a given problem task and which class of algorithms is best suited for real-world problem solving.

  • Use statistical learning theory to combine multiple machine learning models via ensemble methods.

  • Apply best-practices for statistical model evaluation, model selection and algorithm comparisons including suitable statistical hypothesis tests.

  • Use contemporary programming languages and machine learning libraries for implementing machine learning algorithms such that they can be readily applied for practical problem solving.

  • Connect concepts from probability theory with supervised learning by implementing models based on Bayes' theorem.

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
D
F

All recorded terms · compare terms & instructors

Prerequisites

Course map

MATH 320, 321, 340, 341, 345, 375, graduate/professional standing, or declared in Statistics VISP

STAT 451

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

Prerequisite text tree

Professors

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

JOHN GILLETT is recorded teaching in Spring 2022, Fall 2022, Spring 2023, Fall 2023, Spring 2024, Fall 2024, Spring 2025, Fall 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

SEBASTIAN RASCHKA is recorded teaching in Fall 2020, Fall 2021. 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 / 750
LEC 002Classroom Instruction0 / 200

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

Past student reviews · John Gillett

Students found the course material well-organized and useful for building quantitative skills, with instructors noted for caring about both learning and professional development.

Recent recorded grades — Spring 2025: 3.49 GPA, 66.2% A/AB (n=80 letter grades); Fall 2025: 3.54 GPA, 72.9% A/AB (n=96 letter grades); Spring 2026: 3.50 GPA, 69.6% A/AB (n=92 letter grades).

difficulty & workload

Past student reviews · John Gillett

The workload was described as manageable compared to programming courses, though exam problems required careful review and some critical thinking.

Past student reviews · John Gillett

Opinions varied significantly; one student found tests difficult to prepare for, while another criticized the organization of materials and a unique group grading policy.

Topics

  • Pattern classification, regression, clustering, and dimensionality reduction
  • Maximum likelihood estimation, Bayesian decision theory, and nonparametric approaches
  • Ensemble methods and statistical learning theory

Skills

  • Understanding ML subfields and essential algorithms
  • Applying statistical model evaluation and hypothesis testing
  • Implementing ML algorithms with modern programming libraries
  • Combining models via ensemble methods

Grades

Historical instructor

Spring 2027 · Projected

Before grades are released

– average GPA

Approximate 80% prediction interval

About this estimate

The course’s semester-average GPA, not an individual student’s grade. The center uses 5 same-season terms, weighted toward recent results.

The range uses the finite-sample 80th-percentile rank of absolute errors from earlier same-season forecasts. Each forecast uses only records from earlier terms. At least four forecasts are required; bounds are rounded outward and limited to 0–4. This is an empirical estimate: changing instructors or grading policies can reduce its coverage.

4 earlier forecasts · 0.11 GPA average error.

Grades over time

Through Spring 2027

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · 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

1280 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

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
STAT 451 · Spring 2027

Introduction to Machine Learning and Statistical Pattern Classification

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": "1274:932:025602",
    "course_id": "STAT 451",
    "course_uid": "course_64851c516bf8195a88f81cc4",
    "term_id": "1274",
    "source_course_id": "025602",
    "source_subject_id": "932",
    "title": "Introduction to Machine Learning and Statistical Pattern Classification",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Not Applicable"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
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": "518152a46b108b0b402de74960ded2f221304f9fede601440b2c1092274ec017",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}