# PHYSICS 361: Machine Learning in Physics | UW–Madison

[View on UW Courses](https://uwcourses.com/courses/PHYSICS_361)

## Dataset

### revision

e243353dcb7d79b7247ced91d69443ef4c2a6349

### repository

twangodev/uwcourses

### schema version

6

### importer version

7

### projection id

94468b7d4154126ca413937bb4738a6d21e0c2277f6fa397a6078ad80a32384f

### observed at

2026-09-07T15:55:43.033547+00:00

### built at

2026-09-10T23:19:01.870283+00:00

### courses

8951

### current instructors

5754

### limited

false

### terms

* 1272
* 1264
* 1262
* 1254
* 1252
* 1244
* 1242
* 1234
* 1232
* 1224
* 1222
* 1212
* 1204
* 1202
* 1194
* 1192
* 1184
* 1182
* 1174
* 1172
* 1164
* 1162
* 1154
* 1152
* 1144
* 1142
* 1134
* 1132
* 1124
* 1122
* 1114
* 1112
* 1104
* 1102
* 1094
* 1092
* 1084
* 1082
* 1074
* 1072

### term

1272

### departments

| subject   | count |
| --------- | ----- |
| AAE       | 90    |
| ABT       | 20    |
| ACCTIS    | 36    |
| ACTSCI    | 14    |
| AFAERO    | 10    |
| AFRICAN   | 87    |
| AFROAMER  | 71    |
| AGROECOL  | 21    |
| AMERIND   | 53    |
| ANAT\&PHY | 7     |
| ANATOMY   | 2     |
| ANESTHES  | 8     |
| ANSCI     | 57    |
| ANTHRO    | 95    |
| ART       | 125   |
| ARTED     | 10    |
| ARTHIST   | 121   |
| ASIALANG  | 129   |
| ASIAN     | 109   |
| ASIANAM   | 28    |
| ASTRON    | 38    |
| ATMOCN    | 69    |
| BIOCHEM   | 51    |
| BIOCORE   | 10    |
| BIOLOGY   | 14    |
| BIOMDSCI  | 18    |
| BME       | 66    |
| BMI       | 41    |
| BMOLCHEM  | 10    |
| BOTANY    | 69    |
| BSE       | 44    |
| C\&ESOC   | 73    |
| CBE       | 48    |
| CHEM      | 101   |
| CHICLA    | 52    |
| CIVENGR   | 133   |
| CLASSICS  | 47    |
| CNP       | 10    |
| CNSRSCI   | 50    |
| COMARTS   | 137   |
| COMPBIO   | 15    |
| COMPLIT   | 11    |
| COMPSCI   | 138   |
| COUNPSY   | 78    |
| CRB       | 19    |
| CS\&D     | 71    |
| CSCS      | 40    |
| CURRIC    | 193   |
| DANCE     | 93    |
| DERM      | 8     |
| DS        | 88    |
| DYSCI     | 35    |
| ECE       | 155   |
| ECON      | 145   |
| EDPOL     | 114   |
| EDPSYCH   | 97    |
| ELPA      | 70    |
| EMA       | 53    |
| EMERMED   | 17    |
| ENGL      | 206   |
| ENTOM     | 40    |
| ENVIRST   | 148   |
| EP        | 15    |
| EPD       | 67    |
| ESL       | 16    |
| F\&WECOL  | 60    |
| FAMMED    | 22    |
| FINANCE   | 50    |
| FOLKLORE  | 40    |
| FOODSCI   | 43    |
| FRENCH    | 63    |
| GEN\&WS   | 145   |
| GENBUS    | 72    |
| GENECSLR  | 17    |
| GENETICS  | 60    |
| GEOG      | 113   |
| GEOSCI    | 83    |
| GERMAN    | 82    |
| GLE       | 48    |
| GNS       | 38    |
| GREEK     | 27    |
| HDFS      | 41    |
| HEBR-BIB  | 13    |
| HEBR-MOD  | 10    |
| HISTORY   | 233   |
| HISTSCI   | 56    |
| HONCOL    | 13    |
| ILS       | 43    |
| INFOSYS   | 9     |
| INTEGART  | 7     |
| INTEGSCI  | 22    |
| INTER-AG  | 18    |
| INTER-HE  | 11    |
| INTER-LS  | 22    |
| INTEREGR  | 16    |
| INTLBUS   | 21    |
| INTLST    | 50    |
| ISYE      | 83    |
| ITALIAN   | 53    |
| JEWISH    | 56    |
| JOURN     | 88    |
| KINES     | 128   |
| LACIS     | 26    |
| LANDARC   | 61    |
| LATIN     | 24    |
| LAW       | 120   |
| LEGALST   | 48    |
| LINGUIS   | 35    |
| LIS       | 89    |
| LITTRANS  | 78    |
| LSC       | 52    |
| M\&ENVTOX | 7     |
| MARKETNG  | 62    |
| MATH      | 153   |
| MDGENET   | 8     |
| ME        | 130   |
| MEDHIST   | 43    |
| MEDICINE  | 60    |
| MEDIEVAL  | 32    |
| MEDPHYS   | 39    |
| MEDSC-M   | 29    |
| MEDSC-V   | 41    |
| MHR       | 68    |
| MICROBIO  | 45    |
| MILSCI    | 16    |
| MM\&I     | 22    |
| MOLBIOL   | 6     |
| MS\&E     | 59    |
| MUSIC     | 165   |
| MUSPERF   | 126   |
| NAVSCI    | 20    |
| NE        | 44    |
| NEURODPT  | 11    |
| NEUROL    | 7     |
| NEURSURG  | 4     |
| NTP       | 11    |
| NURSING   | 111   |
| NUTRSCI   | 65    |
| OBS\&GYN  | 18    |
| OCCTHER   | 39    |
| ONCOLOGY  | 14    |
| OPHTHALM  | 5     |
| OTM       | 43    |
| PATH      | 34    |
| PATH-BIO  | 29    |
| PEDIAT    | 26    |
| PHARMACY  | 31    |
| PHILOS    | 77    |
| PHMCOL-M  | 11    |
| PHMPRAC   | 45    |
| PHMSCI    | 60    |
| PHYASST   | 37    |
| PHYSICS   | 88    |
| PHYSIOL   | 3     |
| PHYTHER   | 37    |
| PLANTSCI  | 52    |
| PLPATH    | 35    |
| POLISCI   | 192   |
| POPHLTH   | 58    |
| PORTUG    | 33    |
| PSYCH     | 101   |
| PSYCHIAT  | 22    |
| PUBAFFR   | 54    |
| PUBLHLTH  | 23    |
| RADIOL    | 11    |
| REALEST   | 41    |
| RELIGST   | 90    |
| RHABMED   | 9     |
| RMI       | 24    |
| RP\&SE    | 102   |
| S\&APHM   | 17    |
| SCANDST   | 73    |
| SLAVIC    | 89    |
| SOC       | 149   |
| SOCWORK   | 87    |
| SOILSCI   | 46    |
| SPANISH   | 83    |
| SRMED     | 21    |
| STAT      | 94    |
| STDYABRD  | 52    |
| STS       | 8     |
| SURGERY   | 25    |
| SURGSCI   | 33    |
| THEATRE   | 91    |
| URBRPL    | 60    |
| UROLOGY   | 5     |
| ZOOLOGY   | 89    |

## course

### run id

20260907T155543-ce3781c4

### semester

1272

### observed at

2026-09-07 15:55:43.033547+00:00

### record version id

0d9ed02e87f3729eac20ee8de96090171c1c55cd26da3528d957c409bc95db6b

### course id

PHYSICS 361

### course uid

course\_84445b017ecd509bab57c075

### catalog version id

44880d9e3ae69f1c91e75fbb768f3df459439c39d7175dc0a4186855757d99ae

### course number

361

### subjects

* PHYSICS

### title

MACHINE LEARNING IN PHYSICS

### description

A detailed introduction to the use of machine learning techniques in physics. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques.  A selection of physics topics that are particularly amenable to analysis using machine learning will be discussed. These might include processing collider data, classifying astronomical images, solving the Ising model, parameter estimation from physics data sets, learning physical probability distributions, finding string theory compactifications, and finding symbolic physical laws.

### requirements text

MATH 234and (PHYSICS 104,202,208, or248), or graduate/professional standing

### credit offering ids

None recorded.

### llm job id

enrich-8b774950c2b6adfdc46d1b82

### llm output id

03da8019a4e4786f2583b89ed54777c111e1d2b4c9b28e88b86f9737fce356aa

### llm model

nvidia/Qwen3.6-35B-A3B-NVFP4

### llm model revision

1355db6a052410cfd62085d94b58866fd0f2c3c5

### llm task version

14

### llm search status

valid

### llm summary

Introduces machine learning techniques, including probability, optimization, and function fitting, applied to various physics problems.

### llm topics

* Probability theory, statistics, function fitting, parameter inference, and optimization.
* Collider data processing, astronomical image classification, Ising model, parameter estimation, physical probability distributions, string theory compactifications, and symbolic physical laws.

### llm skills

* Applying machine learning, probability, statistics, function fitting, parameter inference, and optimization to physics problems.
* Analyzing specific physics domains such as collider data, astronomical images, and symbolic law discovery using ML.

### llm assumed background

* Calculus of functions of several variables and general physics (electricity, magnetism, modern physics, or computation).
* Multivariable calculus including vector calculus and multiple integrals.
* Calculus-based physics covering electricity, magnetism, optics, and modern physics, with computational components in some tracks.

### llm search phrases

* machine learning physics applications
* physics data analysis machine learning
* optimization physics machine learning
* probability statistics physics machine learning

### llm requirements status

valid

### llm student summary status

valid

### llm experience status

insufficient\_evidence

### catalog variants

None recorded.

### student summary

#### context hash

79b9283e03b59e5ff5a8fb99076b1e3177394d4b897d4b93c407bf98c74afa7d

#### course id

PHYSICS 361

#### current instructors

None recorded.

#### difficulty workload

None recorded.

#### errors

None recorded.

#### historical context

None recorded.

#### message

No course-specific reviews available

#### offered

false

#### profile hash

5cb4dabf887cdbcd8c00d5a1312e10828b95c63f30bc3ea76aea199565390d02

#### quick take

Recent recorded grades — Spring 2024: 3.78 GPA, 88.9% A/AB (n=18 letter grades); Spring 2025: 3.88 GPA, 94.4% A/AB (n=36 letter grades); Spring 2026: 3.74 GPA, 91.7% A/AB (n=48 letter grades).

```json
{
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```

#### student experience

None recorded.

#### task hash

74fb0997943e888960bbc9e47db8c4fd12e4292d55c20509d8d89db6a9910f68

#### teaching history

None recorded.

#### term id

1272

#### term name

2026 Fall

#### version

2

### requirements

#### nodes

```json
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```

#### notes

None recorded.

#### root

n0

#### status

parsed

### instructors

None recorded.

### offerings

None recorded.

### sections

None recorded.

### grade instructors

```json
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    "email": null,
    "first_observed_at": "2026-09-06 23:14:58.172943+00:00",
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    "ratings": {
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      "quality": 4.29,
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      "prior_mean": 3.6596857835465957,
      "prior_weight": 20
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    "grade_statistics": {
      "gpa": 3.249,
      "graded": 910,
      "counts": [
        292,
        197,
        201,
        138,
        74,
        3,
        5
      ],
      "sections": 23
    },
    "instructor_url": "/instructors/MORITZ_MUNCHMEYER"
  },
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    "source": "madgrades",
    "source_instructor_id": "3574840",
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    "identity_status": "source_identified",
    "name": "GARY SHIU",
    "email": null,
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        133,
        20
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]
```

### grades

```json
[
  {
    "run_id": "20260907T155543-ce3781c4",
    "semester": "1272",
    "observed_at": "2026-09-07 15:55:43.033547+00:00",
    "course_id": "PHYSICS 361",
    "course_uid": "course_84445b017ecd509bab57c075",
    "term_id": "1244",
    "term_name": "Spring 2024",
    "instructors": [
      "Gary Shiu",
      "MORITZ MUNCHMEYER"
    ],
    "a": 12,
    "ab": 4,
    "b": 2,
    "bc": 0,
    "c": 0,
    "d": 0,
    "f": 0,
    "satisfactory": 0,
    "unsatisfactory": 0,
    "credit": 0,
    "no_credit": 0,
    "passed": 0,
    "incomplete": 1,
    "no_work": 0,
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  {
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    "semester": "1272",
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    "course_uid": "course_84445b017ecd509bab57c075",
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    "term_name": "Spring 2025",
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      "MORITZ MUNCHMEYER"
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    "a": 29,
    "ab": 5,
    "b": 2,
    "bc": 0,
    "c": 0,
    "d": 0,
    "f": 0,
    "satisfactory": 2,
    "unsatisfactory": 0,
    "credit": 0,
    "no_credit": 0,
    "passed": 0,
    "incomplete": 0,
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    "total": 38,
    "source_aliases": [
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    "run_id": "20260907T155543-ce3781c4",
    "semester": "1272",
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    "course_uid": "course_84445b017ecd509bab57c075",
    "term_id": "1264",
    "term_name": "Spring 2026",
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    "b": 3,
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    "total": 50,
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]
```

### statistics

#### gpa

3.794

#### graded

102

#### counts

* 74
* 20
* 7
* 0
* 0
* 0
* 1

### grade conflicts

None recorded.

### evidence

#### history

* [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course\_84445b017ecd509bab57c075-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course_84445b017ecd509bab57c075-0.json)

#### traces

* [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course\_84445b017ecd509bab57c075-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course_84445b017ecd509bab57c075-0.json)

#### results

* [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course\_84445b017ecd509bab57c075-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course_84445b017ecd509bab57c075-0.json)

### revision

e243353dcb7d79b7247ced91d69443ef4c2a6349

## context

### all

#### term



#### gpa

3.7941176470588234

#### count

102

#### university

##### size

2411

##### gpa Percentile

57

##### count Percentile

54

##### median Count

90

##### histogram

| range   | count | current |
| ------- | ----- | ------- |
| 0.0–0.4 | 0     | false   |
| 0.4–0.8 | 0     | false   |
| 0.8–1.2 | 0     | false   |
| 1.2–1.6 | 0     | false   |
| 1.6–2.0 | 0     | false   |
| 2.0–2.4 | 0     | false   |
| 2.4–2.8 | 17    | false   |
| 2.8–3.2 | 163   | false   |
| 3.2–3.6 | 604   | false   |
| 3.6–4.0 | 1627  | true    |

#### departments

```json
[
  {
    "subject": "PHYSICS",
    "comparison": {
      "size": 35,
      "gpaPercentile": 79,
      "countPercentile": 41,
      "medianCount": 119,
      "histogram": [
        {
          "range": "0.0–0.4",
          "count": 0,
          "current": false
        },
        {
          "range": "0.4–0.8",
          "count": 0,
          "current": false
        },
        {
          "range": "0.8–1.2",
          "count": 0,
          "current": false
        },
        {
          "range": "1.2–1.6",
          "count": 0,
          "current": false
        },
        {
          "range": "1.6–2.0",
          "count": 0,
          "current": false
        },
        {
          "range": "2.0–2.4",
          "count": 0,
          "current": false
        },
        {
          "range": "2.4–2.8",
          "count": 0,
          "current": false
        },
        {
          "range": "2.8–3.2",
          "count": 10,
          "current": false
        },
        {
          "range": "3.2–3.6",
          "count": 11,
          "current": false
        },
        {
          "range": "3.6–4.0",
          "count": 14,
          "current": true
        }
      ]
    }
  }
]
```

### terms

#### 1244

##### term

1244

##### gpa

3.7777777777777777

##### count

18

##### departments

| subject | comparison |
| ------- | ---------- |
| PHYSICS |            |

#### 1254

##### term

1254

##### gpa

3.875

##### count

36

##### university

###### size

1289

###### gpa Percentile

77

###### count Percentile

13

###### median Count

66

###### histogram

| range   | count | current |
| ------- | ----- | ------- |
| 0.0–0.4 | 0     | false   |
| 0.4–0.8 | 0     | false   |
| 0.8–1.2 | 0     | false   |
| 1.2–1.6 | 0     | false   |
| 1.6–2.0 | 0     | false   |
| 2.0–2.4 | 0     | false   |
| 2.4–2.8 | 17    | false   |
| 2.8–3.2 | 128   | false   |
| 3.2–3.6 | 379   | false   |
| 3.6–4.0 | 765   | true    |

##### departments

```json
[
  {
    "subject": "PHYSICS",
    "comparison": {
      "size": 24,
      "gpaPercentile": 96,
      "countPercentile": 13,
      "medianCount": 67.5,
      "histogram": [
        {
          "range": "0.0–0.4",
          "count": 0,
          "current": false
        },
        {
          "range": "0.4–0.8",
          "count": 0,
          "current": false
        },
        {
          "range": "0.8–1.2",
          "count": 0,
          "current": false
        },
        {
          "range": "1.2–1.6",
          "count": 0,
          "current": false
        },
        {
          "range": "1.6–2.0",
          "count": 0,
          "current": false
        },
        {
          "range": "2.0–2.4",
          "count": 0,
          "current": false
        },
        {
          "range": "2.4–2.8",
          "count": 0,
          "current": false
        },
        {
          "range": "2.8–3.2",
          "count": 11,
          "current": false
        },
        {
          "range": "3.2–3.6",
          "count": 2,
          "current": false
        },
        {
          "range": "3.6–4.0",
          "count": 11,
          "current": true
        }
      ]
    }
  }
]
```

#### 1264

##### term

1264

##### gpa

3.7395833333333335

##### count

48

##### university

###### size

1283

###### gpa Percentile

55

###### count Percentile

33

###### median Count

66

###### histogram

| range   | count | current |
| ------- | ----- | ------- |
| 0.0–0.4 | 0     | false   |
| 0.4–0.8 | 0     | false   |
| 0.8–1.2 | 0     | false   |
| 1.2–1.6 | 0     | false   |
| 1.6–2.0 | 0     | false   |
| 2.0–2.4 | 1     | false   |
| 2.4–2.8 | 20    | false   |
| 2.8–3.2 | 130   | false   |
| 3.2–3.6 | 343   | false   |
| 3.6–4.0 | 789   | true    |

##### departments

```json
[
  {
    "subject": "PHYSICS",
    "comparison": {
      "size": 20,
      "gpaPercentile": 89,
      "countPercentile": 26,
      "medianCount": 76.5,
      "histogram": [
        {
          "range": "0.0–0.4",
          "count": 0,
          "current": false
        },
        {
          "range": "0.4–0.8",
          "count": 0,
          "current": false
        },
        {
          "range": "0.8–1.2",
          "count": 0,
          "current": false
        },
        {
          "range": "1.2–1.6",
          "count": 0,
          "current": false
        },
        {
          "range": "1.6–2.0",
          "count": 0,
          "current": false
        },
        {
          "range": "2.0–2.4",
          "count": 0,
          "current": false
        },
        {
          "range": "2.4–2.8",
          "count": 0,
          "current": false
        },
        {
          "range": "2.8–3.2",
          "count": 10,
          "current": false
        },
        {
          "range": "3.2–3.6",
          "count": 3,
          "current": false
        },
        {
          "range": "3.6–4.0",
          "count": 7,
          "current": true
        }
      ]
    }
  }
]
```

### benchmarks

#### all

##### school

###### size

2411

###### gpa

3.6770271983084943

###### top Share

83.11222705251026

###### count

90

##### PHYSICS

###### size

35

###### gpa

3.4846085362114283

###### top Share

70.17709382294345

###### count

119

#### terms

##### 1244

###### school

###### size

1241

###### gpa

3.5975573126929192

###### top Share

78.29036874847135

###### count

65

###### PHYSICS

###### size

21

###### gpa

3.4298442610054316

###### top Share

66.31505990488392

###### count

61

##### 1254

###### school

###### size

1289

###### gpa

3.6126423469389106

###### top Share

79.48050408754871

###### count

66

###### PHYSICS

###### size

24

###### gpa

3.4204976248133576

###### top Share

67.48442638438776

###### count

67.5

##### 1264

###### school

###### size

1283

###### gpa

3.6229729166451925

###### top Share

80.13669149396227

###### count

66

###### PHYSICS

###### size

20

###### gpa

3.3226640805171783

###### top Share

59.84154395185915

###### count

76.5

## instructor Trends

```json
[
  {
    "uid": "instructor_1a25d9658695c6cd202e3138",
    "name": "MORITZ MUNCHMEYER",
    "count": 102,
    "terms": [
      {
        "term": "1244",
        "count": 18,
        "sections": 1,
        "gpa": 3.7777777777777777
      },
      {
        "term": "1254",
        "count": 36,
        "sections": 1,
        "gpa": 3.875
      },
      {
        "term": "1264",
        "count": 48,
        "sections": 1,
        "gpa": 3.7395833333333335
      }
    ]
  },
  {
    "uid": "instructor_1c2dced22d017cf21d25adc3",
    "name": "GARY SHIU",
    "count": 18,
    "terms": [
      {
        "term": "1244",
        "count": 18,
        "sections": 1,
        "gpa": 3.7777777777777777
      }
    ]
  }
]
```

## following

None recorded.
