# COMPSCI 772: Learning Based Image Synthesis and Manipulation | UW–Madison

[View on UW Courses](https://uwcourses.com/courses/COMPSCI_772)

## Dataset

### revision

e243353dcb7d79b7247ced91d69443ef4c2a6349

### repository

twangodev/uwcourses

### schema version

6

### importer version

7

### projection id

52a78527ff09011d88cb04c7d43867d9fbfec2930bf1dd2ec359ebd2844e0b07

### observed at

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

### built at

2026-09-10T19:50:42.743001+00:00

### courses

8951

### current instructors

5754

### limited

false

### terms

* 1272
* 1264
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### 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

9c4e8bfcff4004f7bc17ff2edc19732a20424a0db45c228e598c2f06b97fd6ba

### course id

COMPSCI 772

### course uid

course\_bd3691f4b2e26b4c2f1a11f6

### catalog version id

0cdb038848fafbed5125b763fb7c526b7c8e10c23722db9f28ccf62ae2e9b5e8

### course number

772

### subjects

* COMPSCI

### title

LEARNING BASED IMAGE SYNTHESIS AND MANIPULATION

### description

Introduces machine learning based synthesis and manipulation of visual data (images and videos).  Both classical (e.g., nearest neighbor, filtering) and modern deep learning based (e.g., ConvNets, GANs, Diffusion Models) algorithms will be presented for image representation, synthesis, and manipulation. Usage of self-developed algorithms for image synthesis and manipulation to understand and analyze state-of-the-art techniques, and to identify interesting open questions and future directions.

### requirements text

Graduate/professional standing

### credit offering ids

None recorded.

### llm job id

enrich-8b774950c2b6adfdc46d1b82

### llm output id

0d580a7316176fc3f322057fa57d8f0572e56e489df1a5412d4604e11411bb8f

### llm model

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

### llm model revision

1355db6a052410cfd62085d94b58866fd0f2c3c5

### llm task version

14

### llm search status

valid

### llm summary

COMPSCI 772 introduces machine learning-based synthesis and manipulation of visual data using classical and deep learning algorithms.

### llm topics

* Visual data (images and videos)
* Classical algorithms (nearest neighbor, filtering)
* Modern deep learning models (ConvNets, GANs, Diffusion Models)
* Image representation, synthesis, and manipulation

### llm skills

* Developing algorithms for image synthesis and manipulation
* Analyzing state-of-the-art machine learning techniques
* Identifying open research questions in visual data processing

### llm assumed background

* Graduate or professional student standing

### llm search phrases

* image synthesis deep learning
* GANs diffusion models image manipulation
* visual data machine learning
* ConvNets image representation

### llm requirements status

valid

### llm student summary status

valid

### llm experience status

insufficient\_evidence

### catalog variants

None recorded.

### student summary

#### context hash

e287592eabcc115fd90b7665cb24242520f8ce4addfa361d924dff59857010a6

#### course id

COMPSCI 772

#### 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

None recorded.

#### student experience

None recorded.

#### task hash

74fb0997943e888960bbc9e47db8c4fd12e4292d55c20509d8d89db6a9910f68

#### teaching history

None recorded.

#### term id

1272

#### term name

2026 Fall

#### version

2

### requirements

#### nodes

```json
[
  {
    "children": [],
    "condition": "Graduate/professional standing",
    "course": null,
    "evidence": "Graduate/professional standing",
    "id": "n0",
    "kind": "condition"
  }
]
```

#### notes

None recorded.

#### root

n0

#### status

parsed

### instructors

None recorded.

### offerings

None recorded.

### sections

None recorded.

### grade instructors

None recorded.

### grades

None recorded.

### statistics

#### graded

0

#### counts

* 0
* 0
* 0
* 0
* 0
* 0
* 0

### grade conflicts

None recorded.

### evidence

#### history

* [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course\_bd3691f4b2e26b4c2f1a11f6-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course_bd3691f4b2e26b4c2f1a11f6-0.json)

#### traces

* [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course\_bd3691f4b2e26b4c2f1a11f6-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course_bd3691f4b2e26b4c2f1a11f6-0.json)

#### results

* [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course\_bd3691f4b2e26b4c2f1a11f6-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course_bd3691f4b2e26b4c2f1a11f6-0.json)

### revision

e243353dcb7d79b7247ced91d69443ef4c2a6349

## instructor Trends

None recorded.

## following

None recorded.