Fall 2026

Introduction to Artificial Neural Networks

Introduction to artificial neural networks and their applications in control, pattern recognition, and prediction.

offering recorded3 credits
Recorded instructors · Fall 2026 Ting-Hung Lin

Summary

1 / 6

Historical reviews of Kangwook Lee, Pedro Morgado, Yu Hen Hu: The course is consistently described as difficult with a heavy workload, including extensive homework, group projects, and dense theoretical content requiring strong math backgrounds.

Grade history

↗
average GPA
letter grades
A
AB
B
BC
C
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F

All recorded terms · compare terms & instructors

Prerequisites

Course map

COMP SCI 200, 220, 300, 301, 302, 310, placement into COMP SCI 300, or graduate/professional standing

COMPSCI/ECE/ME 539 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

Professors

Fall 2026
Historical instructors & teaching patterns

Historical reviews of Yu Hen Hu: Yu Hen Hu is described as kind and willing to listen to feedback, but his teaching style is heavily criticized for reading slides and lacking clear explanations. Reviewers report that the course content is dense and difficult, with confusing materials and a final exam that is excessively long and error-prone. Many students found the assignments unmotivating and the grading harsh, leading to poor learning outcomes.

PEDRO MARAVILHA MORGADO is recorded teaching in Fall 2023, Spring 2025. Recorded history may be incomplete and does not establish a future schedule.

YU HU is recorded teaching in Fall 2008, Fall 2010, Fall 2013, Spring 2016, Fall 2017, Fall 2018, Fall 2020, Fall 2021, Spring 2022, Fall 2022, Spring 2023, Fall 2023, Spring 2024. 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

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction81 / 1300
LEC 001Classroom Instruction21 / 1300
LEC 001Classroom Instruction7 / 800

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 Pedro Morgado highlight his approachability and interesting lectures, though the flipped classroom format and heavy workload are noted concerns.

Recent recorded grades — Spring 2025: 3.62 GPA, 84.1% A/AB (n=88 letter grades); Fall 2025: 3.57 GPA, 73.2% A/AB (n=82 letter grades); Spring 2026: 3.23 GPA, 57.1% A/AB (n=35 letter grades).

difficulty & workload

Historical reviews of Kangwook Lee, Pedro Morgado, Yu Hen Hu: The course is consistently described as difficult with a heavy workload, including extensive homework, group projects, and dense theoretical content requiring strong math backgrounds.

Historical reviews of Kangwook Lee, Yu Hen Hu: Students report frustration with unclear lectures, excessive theory over implementation, and poor exam design, particularly under Yu Hen Hu and Kangwook Lee.

Topics

  • Neural network architectures
  • Support vector machines, genetic algorithms, and evolution computing
  • Applications in control, pattern recognition, prediction, and tracking

Skills

  • Theory and application of artificial neural networks and related AI techniques
  • Applying AI models to control, pattern recognition, prediction, and tracking

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 · 0.10 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
COMPSCI/ECE/ME 539 · Fall 2026

Introduction to Artificial Neural Networks

Recorded 2026-09-07
COMPSCI/ECE/ME 539 · Fall 2026

Introduction to Artificial Neural Networks

Recorded 2026-09-07
COMPSCI/ECE/ME 539 · Fall 2026

Introduction to Artificial Neural Networks

Recorded 2026-09-07
Raw records
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    "run_id": "20260907T155543-ce3781c4",
    "semester": "1272",
    "observed_at": "2026-09-07 15:55:43.033547+00:00",
    "offering_id": "1272:266:006422",
    "course_id": "COMPSCI/ECE/ME 539",
    "course_uid": "course_013300c17c3a4f3c90089d51",
    "term_id": "1272",
    "source_course_id": "006422",
    "source_subject_id": "266",
    "title": "Introduction to Artificial Neural Networks",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Fall"
  },
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    "run_id": "20260907T155543-ce3781c4",
    "semester": "1272",
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    "course_id": "COMPSCI/ECE/ME 539",
    "course_uid": "course_013300c17c3a4f3c90089d51",
    "term_id": "1272",
    "source_course_id": "006422",
    "source_subject_id": "320",
    "title": "Introduction to Artificial Neural Networks",
    "credits_min": 3,
    "credits_max": 3,
    "typically_offered": "Fall"
  },
  {
    "run_id": "20260907T155543-ce3781c4",
    "semester": "1272",
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    "offering_id": "1272:612:006422",
    "course_id": "COMPSCI/ECE/ME 539",
    "course_uid": "course_013300c17c3a4f3c90089d51",
    "term_id": "1272",
    "source_course_id": "006422",
    "source_subject_id": "612",
    "title": "Introduction to Artificial Neural Networks",
    "credits_min": 3,
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  }
]
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 2026-09-07

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": "bcdb48aeff1207dbf8e3257d8f31f51e07233741d3047965d80da54011c914de",
  "requirements_status": "needs_review",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}