# COMPSCI 566: Introduction to Computer Vision | UW–Madison [View on UW Courses](https://uwcourses.com/courses/COMPSCI_566) ## 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 709c6682c031c33ebdeb0085f0ef107d991bc0e1bdf206f0e30348b9e2bcbfba ### course id COMPSCI 566 ### course uid course\_3659ad39c7809a6abd08ad56 ### catalog version id e076df959d914b60c8f3416f245900f33080dc5d719ab8f15685aeb480a69d9c ### course number 566 ### subjects * COMPSCI ### title INTRODUCTION TO COMPUTER VISION ### description Topics include image formation, feature detection, motion estimation, image mosaics, 3D shape reconstruction, and object recognition. Applications of these techniques include building 3D maps, creating virtual characters, organizing photo and video databases, human computer interaction, video surveillance, and automatic vehicle navigation. Broad overview of various computer vision and machine learning techniques and sensing and imaging technologies used in computer vision applications. Project-based. ### requirements text COMP SCI 400and (MATH 320,340,341,345or375) and (STAT 311,324,333,340,371,STAT/​MATH  309,431,MATH 331or531) or graduate/professional standing ### credits min 3 ### credits max 3 ### credit offering ids * 1272:266:026547 ### llm job id enrich-8b774950c2b6adfdc46d1b82 ### llm output id d4be9ef5ad58a3b789cb2e97e4be50b3f3576dd942d1ab7b65cbd190f8ffd272 ### llm model nvidia/Qwen3.6-35B-A3B-NVFP4 ### llm model revision 1355db6a052410cfd62085d94b58866fd0f2c3c5 ### llm task version 14 ### llm search status valid ### llm summary Introduction to computer vision covering image formation, feature detection, 3D reconstruction, and object recognition with project-based applications. ### llm topics * Image formation, feature detection, motion estimation, mosaics, 3D shape reconstruction, object recognition. * 3D maps, virtual characters, photo/video databases, HCI, video surveillance, automatic vehicle navigation. ### llm skills * Image formation, feature detection, motion estimation, mosaics, 3D reconstruction, and object recognition. * Applying computer vision techniques to maps, virtual characters, databases, HCI, surveillance, and navigation. ### llm assumed background * Programming fundamentals including data structures, algorithms, and object-oriented design. * Mathematical statistics and probability theory. ### llm search phrases * computer vision applications * image formation feature detection * 3D shape reconstruction object recognition * machine learning computer vision ### llm requirements status valid ### llm student summary status valid ### llm experience status insufficient\_evidence ### catalog variants None recorded. ### student summary #### context hash c67097cbfe4fa897356989718e7407ca7476bc68504b052cc12448d396d8465c #### course id COMPSCI 566 #### current instructors ```json [ { "instructor_uid": "instructor_fae40bcc319ab178a209c988", "message": "No course-specific reviews available", "name": "Mohit Gupta", "review_status": "no_course_reviews", "rmp_instructor_id": "rmp:2140649", "summary": [ { "citations": [ { "course_id": "COMPSCI 566", "run_id": "20260907T155543-ce3781c4", "section_number": 1, "source_course_id": "5bbda582-d5e5-3e98-b020-442208705a16", "source_record": { "entity_id": "5bbda582-d5e5-3e98-b020-442208705a16", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1262", "type": "grade" } ], "text": "Recent recorded grades — Fall 2025: 3.50 GPA, 77.3% A/AB (n=88 letter grades). Includes jointly taught sections." } ] } ] ``` #### difficulty workload None recorded. #### errors None recorded. #### historical context None recorded. #### message No course-specific reviews available #### offered true #### profile hash 5cb4dabf887cdbcd8c00d5a1312e10828b95c63f30bc3ea76aea199565390d02 #### quick take Recent recorded grades — Fall 2025: 3.50 GPA, 77.3% A/AB (n=88 letter grades). ```json { "citations": [ { "course_id": "COMPSCI 566", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "5bbda582-d5e5-3e98-b020-442208705a16", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1262", "type": "grade" } ] } ``` #### student experience None recorded. #### task hash 74fb0997943e888960bbc9e47db8c4fd12e4292d55c20509d8d89db6a9910f68 #### teaching history MOHIT GUPTA is recorded teaching in Fall 2025. 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"minimum_grade": null, "subjects": [ "STAT", "MATH" ], "timing": "prior" }, "evidence": "STAT/MATH 309", "id": "n17", "kind": "course" }, { "children": [], "condition": null, "course": { "course_number": 431, "minimum_grade": null, "subjects": [ "STAT", "MATH" ], "timing": "prior" }, "evidence": "431", "id": "n18", "kind": "course" }, { "children": [], "condition": null, "course": { "course_number": 331, "minimum_grade": null, "subjects": [ "MATH" ], "timing": "prior" }, "evidence": "MATH 331", "id": "n19", "kind": "course" }, { "children": [], "condition": null, "course": { "course_number": 531, "minimum_grade": null, "subjects": [ "MATH" ], "timing": "prior" }, "evidence": "531", "id": "n20", "kind": "course" } ] ``` #### notes None recorded. #### root n0 #### status parsed ### instructors ```json [ { "instructor_uid": "instructor_fae40bcc319ab178a209c988", "source": "enrollment", "source_instructor_id": "mgupta37", "identity_basis": "netid", "identity_status": "source_identified", 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"term_id": "1272", "source_course_id": "026547", "source_subject_id": "266", "title": "Introduction to Computer Vision", "credits_min": 3, "credits_max": 3, "typically_offered": "Not Applicable" } ] ``` ### sections ```json [ { "run_id": "20260907T155543-ce3781c4", "observed_at": "2026-09-07 15:55:43.033547+00:00", "section_uid": "uw-section:1272:31362", "term_id": "1272", "source_section_id": "31362", "identity_basis": "class_number", "section_number": "001", "section_type": "LEC", "instruction_mode": "Online Only", "capacity": 100, "enrolled": 97, "waitlisted": 3, "start_date": "2026-09-02 05:00:00+00:00", "end_date": "2026-12-09 06:00:00+00:00" } ] ``` ### grade instructors ```json [ { "instructor_uid": "instructor_9f9dc36473c6c53a5b11584e", "source": "madgrades", "source_instructor_id": "6101096", "identity_basis": "source_id", "identity_status": "source_identified", "name": "ERIC BRANDT", "email": null, "first_observed_at": "2026-09-06 23:14:58.172943+00:00", "last_observed_at": "2026-09-07 15:55:43.033547+00:00", "grade_statistics": { "gpa": 3.417, "graded": 320, "counts": [ 120, 81, 87, 22, 5, 4, 1 ], "sections": 3 }, "instructor_url": "/instructors/ERIC_BRANDT--instructor_9f9dc36473c6c53a5b11584e" }, { "instructor_uid": "instructor_d75535f7dc798d8fdfb14c88", "source": "madgrades", "source_instructor_id": "6764381", "identity_basis": "source_id", "identity_status": "source_identified", "name": "SUNGJIN CHEONG", "email": null, "first_observed_at": "2026-09-06 23:14:58.172943+00:00", "last_observed_at": "2026-09-07 15:55:43.033547+00:00", "grade_statistics": { "gpa": 3.634, "graded": 146, "counts": [ 95, 28, 10, 5, 3, 4, 1 ], "sections": 2 }, "instructor_url": "/instructors/SUNGJIN_CHEONG--instructor_d75535f7dc798d8fdfb14c88" }, { "instructor_uid": "instructor_fb28be19ca59149fc4de281a", "source": "madgrades", "source_instructor_id": "5573480", "identity_basis": "source_id", "identity_status": "source_identified", "name": "MOHIT GUPTA", "email": null, "first_observed_at": "2026-09-06 23:14:58.172943+00:00", "last_observed_at": "2026-09-07 15:55:43.033547+00:00", "ratings": { "review_count": 3, "quality": 2.67, "difficulty": 3.33, "quality_count": 3, "difficulty_count": 3, "profile_id": "rmp:2140649", "source_url": "https://www.ratemyprofessors.com/professor/2140649", "match_basis": "exact_name", "observed_at": "2026-09-07 16:16:41.159614+00:00", "courses": { "course_454ae4ccf39aa0d54593f0ce": { "review_count": 1, "quality": 4, "difficulty": 4, "quality_count": 1, "difficulty_count": 1 } }, "bayesian_quality": 3.530596333518779, "prior_mean": 3.6596857835465957, "prior_weight": 20 }, "grade_statistics": { "gpa": 3.737, "graded": 774, "counts": [ 558, 132, 34, 25, 14, 6, 5 ], "sections": 74 }, "instructor_url": "/instructors/MOHIT_GUPTA--instructor_fb28be19ca59149fc4de281a" }, { "instructor_uid": "instructor_fae40bcc319ab178a209c988", "source": "enrollment", "source_instructor_id": "mgupta37", "identity_basis": "netid", "identity_status": "source_identified", "name": "Mohit Gupta", "email": "MGUPTA37@WISC.EDU", "first_observed_at": "2026-09-07 15:55:43.033547+00:00", "last_observed_at": "2026-09-07 15:55:43.033547+00:00", "ratings": { "review_count": 3, "quality": 2.67, "difficulty": 3.33, "quality_count": 3, "difficulty_count": 3, "profile_id": "rmp:2140649", "source_url": "https://www.ratemyprofessors.com/professor/2140649", "match_basis": "exact_name", "observed_at": "2026-09-07 16:16:41.159614+00:00", "courses": { "course_454ae4ccf39aa0d54593f0ce": { "review_count": 1, "quality": 4, "difficulty": 4, "quality_count": 1, "difficulty_count": 1 } }, "bayesian_quality": 3.530596333518779, "prior_mean": 3.6596857835465957, "prior_weight": 20 }, "instructor_url": "/instructors/MOHIT_GUPTA" } ] ``` ### grades ```json [ { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "COMPSCI 566", "course_uid": "course_3659ad39c7809a6abd08ad56", "term_id": "1262", "term_name": "Fall 2025", "instructors": [ "ERIC BRANDT", "Mohit Gupta", "SUNGJIN CHEONG" ], "a": 47, "ab": 21, "b": 8, "bc": 5, "c": 3, "d": 4, "f": 0, "satisfactory": 2, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 90, "source_aliases": [ "COMPSCI 566" ] } ] ``` ### statistics #### gpa 3.5 #### graded 88 #### counts * 47 * 21 * 8 * 5 * 3 * 4 * 0 ### grade conflicts None recorded. ### evidence #### history * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course\_3659ad39c7809a6abd08ad56-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course_3659ad39c7809a6abd08ad56-0.json) #### traces * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course\_3659ad39c7809a6abd08ad56-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course_3659ad39c7809a6abd08ad56-0.json) #### meetings * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/meetings/course\_3659ad39c7809a6abd08ad56-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/meetings/course_3659ad39c7809a6abd08ad56-0.json) #### results * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course\_3659ad39c7809a6abd08ad56-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course_3659ad39c7809a6abd08ad56-0.json) ### revision e243353dcb7d79b7247ced91d69443ef4c2a6349 ## context ### all #### term #### gpa 3.5 #### count 88 #### university ##### size 1320 ##### gpa Percentile 29 ##### count Percentile 59 ##### median Count 70 ##### 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 | 8 | false | | 2.8–3.2 | 133 | false | | 3.2–3.6 | 381 | true | | 3.6–4.0 | 797 | false | #### departments ```json [ { "subject": "COMPSCI", "comparison": { "size": 53, "gpaPercentile": 42, "countPercentile": 54, "medianCount": 82, "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": 22, "current": true }, { "range": "3.6–4.0", "count": 21, "current": false } ] } } ] ``` ### terms #### 1262 ##### term 1262 ##### gpa 3.5 ##### count 88 ##### university ###### size 1320 ###### gpa Percentile 29 ###### count Percentile 59 ###### median Count 70 ###### 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 | 8 | false | | 2.8–3.2 | 133 | false | | 3.2–3.6 | 381 | true | | 3.6–4.0 | 797 | false | ##### departments ```json [ { "subject": "COMPSCI", "comparison": { "size": 53, "gpaPercentile": 42, "countPercentile": 54, "medianCount": 82, "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": 22, "current": true }, { "range": "3.6–4.0", "count": 21, "current": false } ] } } ] ``` ### benchmarks #### all ##### school ###### size 1320 ###### gpa 3.628825763035853 ###### top Share 80.21118846327654 ###### count 70 ##### COMPSCI ###### size 53 ###### gpa 3.533845597774486 ###### top Share 75.31824598445624 ###### count 82 #### terms ##### 1262 ###### school ###### size 1320 ###### gpa 3.628825763035853 ###### top Share 80.21118846327654 ###### count 70 ###### COMPSCI ###### size 53 ###### gpa 3.533845597774486 ###### top Share 75.31824598445624 ###### count 82 ## instructor Trends ```json [ { "uid": "instructor_9f9dc36473c6c53a5b11584e", "name": "ERIC BRANDT", "count": 88, "terms": [ { "term": "1262", "count": 88, "sections": 1, "gpa": 3.5 } ] }, { "uid": "instructor_d75535f7dc798d8fdfb14c88", "name": "SUNGJIN CHEONG", "count": 88, "terms": [ { "term": "1262", "count": 88, "sections": 1, "gpa": 3.5 } ] }, { "uid": "instructor_fb28be19ca59149fc4de281a", "name": "MOHIT GUPTA", "count": 88, "terms": [ { "term": "1262", "count": 88, "sections": 1, "gpa": 3.5 } ] } ] ``` ## following None recorded.