Fall 2026

Introduction to Machine Learning and Statistical Pattern Classification

Covers pattern classification, regression, clustering, and dimensionality reduction with a focus on statistical evaluation and Python implementation.

offering recorded3 credits
Recorded instructors · Fall 2026 Xiao LuoYaling Hong

Summary

1 / 6

Historical reviews of John Gillett, Sebastian Raschka: Workload is generally manageable with recorded lectures, though Gillett's exams can be difficult and require careful review, while one review cites unorganized materials and confusing group policies.

Grade history

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average GPA
letter grades
A
AB
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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

Fall 2026

No course-specific feedback yet.

Historical instructors & teaching patterns

Historical reviews for Sebastian Raschka describe him as an inspiring, patient, and responsive instructor who made course materials and projects engaging. Students noted his flexibility and clear communication, which motivated them to pursue further studies in machine learning.

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

Fall 2026

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SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction83 / 980
LEC 002Classroom Instruction1 / 20

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

Historical reviews for Sebastian Raschka and John Gillett describe them as caring, patient, and highly rated instructors who support student 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

Historical reviews of John Gillett, Sebastian Raschka: Workload is generally manageable with recorded lectures, though Gillett's exams can be difficult and require careful review, while one review cites unorganized materials and confusing group policies.

Historical reviews of John Gillett, Sebastian Raschka: Students value the inspirational content, flexible online options, and generous grading curves, though one reviewer found Gillett's teaching methods unorthodox and his website difficult to navigate.

Topics

  • Pattern classification, regression, clustering, and dimensionality reduction.
  • Maximum likelihood estimation, Bayesian decision theory, algorithmic and nonparametric approaches.
  • Evaluation of machine learning models using statistical methods.

Skills

  • Evaluating machine learning models using statistical methods.
  • Applying statistical pattern classification, maximum likelihood estimation, and Bayesian decision theory.
  • Implementing machine learning algorithms using Python open-source libraries.

Grades

Historical instructor

Fall 2026 · 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 · 0.20 GPA average error.

Grades over time

Through Fall 2026

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

1283 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 · Fall 2026

Introduction to Machine Learning and Statistical Pattern Classification

Recorded 2026-09-07
Raw records
[
  {
    "run_id": "20260907T155543-ce3781c4",
    "semester": "1272",
    "observed_at": "2026-09-07 15:55:43.033547+00:00",
    "offering_id": "1272:932:025602",
    "course_id": "STAT 451",
    "course_uid": "course_64851c516bf8195a88f81cc4",
    "term_id": "1272",
    "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.

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Dataset & model

Recorded 2026-09-07

Download the original dataset
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "726a29e0b92f471b064997ee9153faaf8369e2e90cdfaab837c6e8bc734a120d",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}