Spring 2027

Introduction to Artificial Neural Networks

Introduction to Artificial Neural Networks covering theory and applications including MLPs, CNNs, RNNs, SVMs, and genetic algorithms for control, pattern recognition, and object detection.

offering recorded3 credits
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 6

Past student reviews · Pedro Morgado

The course had a huge workload with many homeworks, exercises, and a group project. The first half was very difficult without a strong background in Linear Algebra or Calc 3.

What you’ll be able to do

  • Identify if a given data analysis task is a pattern classification problem or a model approximation problem.

  • Apply multi-layer perceptron neural network training algorithm to develop artificial neural network (ANN) based pattern classifiers and data predictors.

  • Apply deep learning network for pattern classification

  • Apply support vector machine (SVM) to develop pattern classifiers.

  • Apply self-organization map and k-means to perform clustering operations of a given data set.

  • Apply stochastic optimization methods, including simulated annealing, genetic algorithm and random search to solve a discrete optimization problem.

Source: UW Guide 1 UW Guide 2 UW Guide 3

Grade history

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

All recorded terms · compare terms & instructors

Prerequisites

Course map

COMP SCI 200, 220, 300, 301, 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

Spring 2027

No instructors recorded for this selection.

Historical instructors & teaching patterns

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

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 600
LEC 001Classroom Instruction0 / 600
LEC 001Classroom Instruction0 / 600

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 · Pedro Morgado

Students described the content as great and lectures as interesting, though one noted the flipped classroom format was not to their taste.

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

Past student reviews · Pedro Morgado

The course had a huge workload with many homeworks, exercises, and a group project. The first half was very difficult without a strong background in Linear Algebra or Calc 3.

Past student reviews · Pedro Morgado

Prof. Morgado was very nice and approachable, always answering questions and providing good feedback, which students recommended highly.

Topics

  • Neural network architectures: MLP, SOM, CNN, RNN
  • Machine learning methods: SVM, Genetic Algorithms, Evolutionary Computing
  • Applications: Control, Pattern Recognition, Prediction, Object Detection

Skills

  • Identify pattern classification vs. model approximation tasks
  • Develop ANN-based pattern classifiers and data predictors
  • Perform clustering using self-organization maps and k-means
  • Solve discrete optimization problems with stochastic 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 · 0.17 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.

Download part 1
Selected offering source records
COMPSCI/ECE/ME 539 · Spring 2027

Introduction to Artificial Neural Networks

Recorded 2026-10-14
COMPSCI/ECE/ME 539 · Spring 2027

Introduction to Artificial Neural Networks

Recorded 2026-10-14
COMPSCI/ECE/ME 539 · Spring 2027

Introduction to Artificial Neural Networks

Recorded 2026-10-14
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Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
Download part 1
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 1Download part 2Download part 3
Model & dataset provenance 
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  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
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