Spring 2027

Precision Measurements

General concepts for predicting, characterizing, and reducing noise in measurements. Address the key questions of all experimentalists: (1) How can I improve my signal-to-noise ratio? (2) What is the ultimate detection limit of my measurement approach? Knowledge of Matlab programming and basic circuit design [such asE C E 230] is required.

no offering record for this termCredits unavailable
Recorded instructors · Spring 2027 No instructors listed

Summary

1 / 4

Recent recorded grades — Fall 2023: 3.88 GPA, 100.0% A/AB (n=12 letter grades); Fall 2024: 3.96 GPA, 100.0% A/AB (n=28 letter grades); Fall 2025: 4.00 GPA, 100.0% A/AB (n=24 letter grades).

What you’ll be able to do

  • Differentiate between different sources of measurement noise

  • Employ noise circuit analysis to predict the detection limit of a measurement

  • Characterize measurement noise in the frequency domain

  • Design and test analog filter circuits

  • Implement lock-in detection techniques to measure small signals

Source: UW Guide

Grade history

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average GPA
letter grades
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Prerequisites

Course map

Graduate/professional standing

  • Graduate/professional standing
ME 768

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  • Graduate/professional standing

Professors

Spring 2027

No instructors recorded for this selection.

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Calendar & sections

Spring 2027

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Student experience

the class

Recent recorded grades — Fall 2023: 3.88 GPA, 100.0% A/AB (n=12 letter grades); Fall 2024: 3.96 GPA, 100.0% A/AB (n=28 letter grades); Fall 2025: 4.00 GPA, 100.0% A/AB (n=24 letter grades).

difficulty & workload

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Grades

Latest available · Fall 2025— 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 Fall 2025

More grade details Grade mix, volume & source data

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

Sources & history

UW–Madison

Catalog & offerings

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Dataset & model

Recorded 2026-10-14

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Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
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Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "3043918b08bf6d4a6a7a3bddf90b54ae987c28a70ea866d1529a944fea14fa03",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}