# BMI/COMPSCI 771: Learning Based Methods for Computer Vision | UW–Madison [View on UW Courses](https://uwcourses.com/courses/BMI_COMPSCI_771) ## 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 * 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 1b987080f53d90b39639a7ce104a2d71b0be478e8a03a7b742e1ff4cf41f364c ### course id BMI/COMPSCI 771 ### course uid course\_97ee8b016eb845f04b422df7 ### catalog version id 2251b86ceb70cac3118f91e76648a17929204bee2c631763d67c455d8c18b50c ### course number 771 ### subjects * BMI * COMPSCI ### title LEARNING BASED METHODS FOR COMPUTER VISION ### description Addresses the problems of representation and reasoning for large amounts of visual data, including images and videos, medical imaging data, and their associated tags or text descriptions. Introduces deep learning in the context of computer vision. Covers topics on visual recognition using deep models, such as image classification, object detection, human pose estimation, action recognition, 3D understanding, and medical image analysis. Emphasizes the design of vision and learning algorithms and models, as well as their practical implementations. Strongly recommended to have knowledge in computer vision or machine learning \[such asCOMP SCI 540] or medical image analysis \[such as B M I /COMP SCI/​B M I  567]. ### requirements text Graduate/professional standing ### credits min 3 ### credits max 3 ### credit offering ids * 1272:210:026314 * 1272:266:026314 ### llm job id enrich-8b774950c2b6adfdc46d1b82 ### llm output id 53bc56f3a2f97c8bdcd454d14d48ed8ba0df25b273a7d6061e5b58543d8ce857 ### llm model nvidia/Qwen3.6-35B-A3B-NVFP4 ### llm model revision 1355db6a052410cfd62085d94b58866fd0f2c3c5 ### llm task version 14 ### llm search status valid ### llm summary Covers deep learning for computer vision tasks like image classification, object detection, and medical image analysis. ### llm topics * Image classification and object detection * Human pose estimation and action recognition * 3D understanding * Medical image analysis ### llm skills * Design of vision and learning algorithms and models * Representation and reasoning for large visual data * Visual recognition using deep models ### llm assumed background * Knowledge in computer vision, machine learning, or medical image analysis * Machine learning and probabilistic reasoning fundamentals * Medical image analysis techniques ### llm search phrases * deep learning computer vision * medical image analysis graduate course * visual recognition algorithms * BMI COMPSCI 771 prerequisites ### llm requirements status valid ### llm student summary status valid ### llm experience status insufficient\_evidence ### catalog variants None recorded. ### student summary #### context hash 756f9b1b20ada49a883a2560be9e0e00f22c21e8e122ff76cb4f3dbdf2d01032 #### course id BMI/COMPSCI 771 #### current instructors ```json [ { "instructor_uid": "instructor_1c875be71e39de81fc8f6631", "message": "No course-specific reviews available", "name": "Yin Li", "review_status": "no_course_reviews", "rmp_instructor_id": "rmp:3015449", "summary": [ { "citations": [ { "course_id": "BMI/COMPSCI 771", "run_id": "20260907T155543-ce3781c4", "section_number": 1, "source_course_id": "472d0ebc-2a9e-3113-8552-93d856fa0d53", "source_record": { "entity_id": "472d0ebc-2a9e-3113-8552-93d856fa0d53", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1242", "type": "grade" }, { "course_id": "BMI/COMPSCI 771", "run_id": "20260907T155543-ce3781c4", "section_number": 1, "source_course_id": "472d0ebc-2a9e-3113-8552-93d856fa0d53", "source_record": { "entity_id": "472d0ebc-2a9e-3113-8552-93d856fa0d53", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1252", "type": "grade" }, { "course_id": "BMI/COMPSCI 771", "run_id": "20260907T155543-ce3781c4", "section_number": 1, "source_course_id": "472d0ebc-2a9e-3113-8552-93d856fa0d53", "source_record": { "entity_id": "472d0ebc-2a9e-3113-8552-93d856fa0d53", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1262", "type": "grade" } ], "text": "Recent recorded grades — Fall 2023: 3.91 GPA, 100.0% A/AB (n=35 letter grades); Fall 2024: 4.00 GPA, 100.0% A/AB (n=43 letter grades); Fall 2025: 3.99 GPA, 100.0% A/AB (n=51 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 2023: 3.91 GPA, 100.0% A/AB (n=35 letter grades); Fall 2024: 4.00 GPA, 100.0% A/AB (n=43 letter grades); Fall 2025: 3.99 GPA, 100.0% A/AB (n=51 letter grades). ```json { "citations": [ { "course_id": "BMI/COMPSCI 771", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "472d0ebc-2a9e-3113-8552-93d856fa0d53", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1242", "type": "grade" }, { "course_id": "BMI/COMPSCI 771", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "472d0ebc-2a9e-3113-8552-93d856fa0d53", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1252", "type": "grade" }, { "course_id": "BMI/COMPSCI 771", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "472d0ebc-2a9e-3113-8552-93d856fa0d53", "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 YIN LI is recorded teaching in Fall 2022, Fall 2023, Fall 2024, Fall 2025. Recorded history may be incomplete and does not establish a future schedule. ```json { "citations": [ { "course_id": "BMI/COMPSCI 771", "run_id": "20260907T155543-ce3781c4", "section_number": 1, "source_course_id": "472d0ebc-2a9e-3113-8552-93d856fa0d53", "source_record": { "entity_id": "472d0ebc-2a9e-3113-8552-93d856fa0d53", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1232", "type": "grade" }, { "course_id": "BMI/COMPSCI 771", "run_id": "20260907T155543-ce3781c4", "section_number": 1, "source_course_id": "472d0ebc-2a9e-3113-8552-93d856fa0d53", "source_record": { "entity_id": "472d0ebc-2a9e-3113-8552-93d856fa0d53", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1242", "type": "grade" }, { "course_id": "BMI/COMPSCI 771", "run_id": "20260907T155543-ce3781c4", "section_number": 1, "source_course_id": "472d0ebc-2a9e-3113-8552-93d856fa0d53", "source_record": { "entity_id": "472d0ebc-2a9e-3113-8552-93d856fa0d53", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1252", "type": "grade" }, { "course_id": "BMI/COMPSCI 771", "run_id": "20260907T155543-ce3781c4", "section_number": 1, "source_course_id": "472d0ebc-2a9e-3113-8552-93d856fa0d53", "source_record": { "entity_id": "472d0ebc-2a9e-3113-8552-93d856fa0d53", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1262", "type": "grade" } ] } ``` #### 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 * The course requires graduate or professional standing. #### root n0 #### status parsed ### instructors | instructor\_uid | source | source\_instructor\_id | identity\_basis | identity\_status | name | email | first\_observed\_at | last\_observed\_at | instructor\_url | | ------------------------------------ | ---------- | ---------------------- | --------------- | ------------------ | ------ | ---------------- | -------------------------------- | -------------------------------- | -------------------- | | instructor\_1c875be71e39de81fc8f6631 | enrollment | li968 | netid | source\_identified | Yin Li | YIN.LI\@WISC.EDU | 2026-09-07 15:55:43.033547+00:00 | 2026-09-07 15:55:43.033547+00:00 | /instructors/YIN\_LI | ### offerings ```json [ { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "offering_id": "1272:210:026314", "course_id": "BMI/COMPSCI 771", "course_uid": "course_97ee8b016eb845f04b422df7", "term_id": "1272", "source_course_id": "026314", "source_subject_id": "210", "title": "Learning Based Methods for Computer Vision", "credits_min": 3, "credits_max": 3, "typically_offered": "Not Applicable" }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "offering_id": "1272:266:026314", "course_id": "BMI/COMPSCI 771", "course_uid": "course_97ee8b016eb845f04b422df7", "term_id": "1272", "source_course_id": "026314", "source_subject_id": "266", "title": "Learning Based Methods for 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:1272:26321", "term_id": "1272", "source_section_id": "26321", "identity_basis": "class_number", "section_number": "001", "section_type": "LEC", "instruction_mode": "Classroom Instruction", "capacity": 65, "enrolled": 4, "waitlisted": 0, "start_date": "2026-09-02 05:00:00+00:00", "end_date": "2026-12-09 06:00:00+00:00" }, { "run_id": "20260907T155543-ce3781c4", "observed_at": "2026-09-07 15:55:43.033547+00:00", "section_uid": "uw-section:1272:26334", "term_id": "1272", "source_section_id": "26334", "identity_basis": "class_number", "section_number": "001", "section_type": "LEC", "instruction_mode": "Classroom Instruction", "capacity": 65, "enrolled": 51, "waitlisted": 0, "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_56e6fe70165f8f15a38e4b22", "source": "madgrades", "source_instructor_id": "6210551", "identity_basis": "source_id", "identity_status": "source_identified", "name": "ABRAR AFZAL MAJEEDI", "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": 3.891, "graded": 46, "counts": [ 37, 8, 1, 0, 0, 0, 0 ], "sections": 1 }, "instructor_url": "/instructors/ABRAR_AFZAL_MAJEEDI" }, { "instructor_uid": "instructor_fed8b1f5a10ba1f2cb2cd3d7", "source": "madgrades", "source_instructor_id": "6209934", "identity_basis": "source_id", "identity_status": "source_identified", "name": "YIN LI", "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": 3.79, "graded": 431, "counts": [ 324, 64, 21, 15, 6, 1, 0 ], "sections": 80 }, "instructor_url": "/instructors/YIN_LI--instructor_fed8b1f5a10ba1f2cb2cd3d7" }, { "instructor_uid": "instructor_bbcb899185f84531e6c8a4ea", "source": "madgrades", "source_instructor_id": "5615621", "identity_basis": "source_id", "identity_status": "source_identified", "name": "CAMERON RUGGLES", "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": 3.303, "graded": 607, "counts": [ 247, 106, 143, 54, 34, 14, 9 ], "sections": 4 }, "instructor_url": "/instructors/CAMERON_RUGGLES--instructor_bbcb899185f84531e6c8a4ea" }, { "instructor_uid": "instructor_1c875be71e39de81fc8f6631", "source": "enrollment", "source_instructor_id": "li968", "identity_basis": "netid", "identity_status": "source_identified", "name": "Yin Li", "email": "YIN.LI@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", "instructor_url": "/instructors/YIN_LI" } ] ``` ### grades ```json [ { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "BMI/COMPSCI 771", "course_uid": "course_97ee8b016eb845f04b422df7", "term_id": "1232", "term_name": "Fall 2022", "instructors": [ "ABRAR AFZAL MAJEEDI", "Yin Li" ], "a": 37, "ab": 8, "b": 1, "bc": 0, "c": 0, "d": 0, "f": 0, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 46, "source_aliases": [ "BMI/COMPSCI 771" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "BMI/COMPSCI 771", "course_uid": "course_97ee8b016eb845f04b422df7", "term_id": "1242", "term_name": "Fall 2023", "instructors": [ "CAMERON RUGGLES", "Yin Li" ], "a": 29, "ab": 6, "b": 0, "bc": 0, "c": 0, "d": 0, "f": 0, "satisfactory": 1, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 36, "source_aliases": [ "BMI/COMPSCI 771" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "BMI/COMPSCI 771", "course_uid": "course_97ee8b016eb845f04b422df7", "term_id": "1252", "term_name": "Fall 2024", "instructors": [ "Yin Li" ], "a": 43, "ab": 0, "b": 0, "bc": 0, "c": 0, "d": 0, "f": 0, "satisfactory": 1, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 44, "source_aliases": [ "BMI/COMPSCI 771" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "BMI/COMPSCI 771", "course_uid": "course_97ee8b016eb845f04b422df7", "term_id": "1262", "term_name": "Fall 2025", "instructors": [ "Yin Li" ], "a": 50, "ab": 1, "b": 0, "bc": 0, "c": 0, "d": 0, "f": 0, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 51, "source_aliases": [ "BMI/COMPSCI 771" ] } ] ``` ### statistics #### gpa 3.951 #### graded 175 #### counts * 159 * 15 * 1 * 0 * 0 * 0 * 0 ### grade conflicts None recorded. ### evidence #### history * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course\_97ee8b016eb845f04b422df7-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course_97ee8b016eb845f04b422df7-0.json) #### traces * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course\_97ee8b016eb845f04b422df7-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course_97ee8b016eb845f04b422df7-0.json) #### meetings * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/meetings/course\_97ee8b016eb845f04b422df7-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/meetings/course_97ee8b016eb845f04b422df7-0.json) #### results * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course\_97ee8b016eb845f04b422df7-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course_97ee8b016eb845f04b422df7-0.json) ### revision e243353dcb7d79b7247ced91d69443ef4c2a6349 ## context ### all #### term #### gpa 3.9514285714285715 #### count 175 #### university ##### size 2734 ##### gpa Percentile 83 ##### count Percentile 67 ##### median Count 105 ##### 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 | 6 | false | | 2.8–3.2 | 183 | false | | 3.2–3.6 | 704 | false | | 3.6–4.0 | 1840 | true | #### departments ```json [ { "subject": "BMI", "comparison": { "size": 13, "gpaPercentile": 58, "countPercentile": 83, "medianCount": 50, "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": 0, "current": false }, { "range": "3.2–3.6", "count": 2, "current": false }, { "range": "3.6–4.0", "count": 11, "current": true } ] } }, { "subject": "COMPSCI", "comparison": { "size": 80, "gpaPercentile": 90, "countPercentile": 49, "medianCount": 175.5, "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": 12, "current": false }, { "range": "3.2–3.6", "count": 26, "current": false }, { "range": "3.6–4.0", "count": 42, "current": true } ] } } ] ``` ### terms #### 1232 ##### term 1232 ##### gpa 3.891304347826087 ##### count 46 ##### university ###### size 1216 ###### gpa Percentile 81 ###### count Percentile 30 ###### 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 | 2 | false | | 2.4–2.8 | 14 | false | | 2.8–3.2 | 174 | false | | 3.2–3.6 | 356 | false | | 3.6–4.0 | 670 | true | ##### departments ```json [ { "subject": "BMI", "comparison": null }, { "subject": "COMPSCI", "comparison": { "size": 50, "gpaPercentile": 86, "countPercentile": 14, "medianCount": 82.5, "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": 9, "current": false }, { "range": "3.2–3.6", "count": 19, "current": false }, { "range": "3.6–4.0", "count": 22, "current": true } ] } } ] ``` #### 1242 ##### term 1242 ##### gpa 3.914285714285714 ##### count 35 ##### university ###### size 1295 ###### gpa Percentile 83 ###### count Percentile 13 ###### median Count 67 ###### 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 | 2 | false | | 2.4–2.8 | 11 | false | | 2.8–3.2 | 162 | false | | 3.2–3.6 | 394 | false | | 3.6–4.0 | 726 | true | ##### departments ```json [ { "subject": "BMI", "comparison": null }, { "subject": "COMPSCI", "comparison": { "size": 52, "gpaPercentile": 88, "countPercentile": 14, "medianCount": 99, "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": 1, "current": false }, { "range": "2.8–3.2", "count": 9, "current": false }, { "range": "3.2–3.6", "count": 20, "current": false }, { "range": "3.6–4.0", "count": 22, "current": true } ] } } ] ``` #### 1252 ##### term 1252 ##### gpa 4 ##### count 43 ##### university ###### size 1333 ###### gpa Percentile 96 ###### count Percentile 26 ###### median Count 69 ###### 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 | 14 | false | | 2.8–3.2 | 115 | false | | 3.2–3.6 | 431 | false | | 3.6–4.0 | 773 | true | ##### departments ```json [ { "subject": "BMI", "comparison": null }, { "subject": "COMPSCI", "comparison": { "size": 54, "gpaPercentile": 96, "countPercentile": 23, "medianCount": 101, "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": 1, "current": false }, { "range": "2.8–3.2", "count": 8, "current": false }, { "range": "3.2–3.6", "count": 23, "current": false }, { "range": "3.6–4.0", "count": 22, "current": true } ] } } ] ``` #### 1262 ##### term 1262 ##### gpa 3.9901960784313726 ##### count 51 ##### university ###### size 1320 ###### gpa Percentile 95 ###### count Percentile 33 ###### 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 | false | | 3.6–4.0 | 797 | true | ##### departments ```json [ { "subject": "BMI", "comparison": null }, { "subject": "COMPSCI", "comparison": { "size": 53, "gpaPercentile": 96, "countPercentile": 21, "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": false }, { "range": "3.6–4.0", "count": 21, "current": true } ] } } ] ``` ### benchmarks #### all ##### school ###### size 2734 ###### gpa 3.6828436876066126 ###### top Share 83.37648184927653 ###### count 105 ##### BMI ###### size 13 ###### gpa 3.831997279357762 ###### top Share 92.65525643015266 ###### count 50 ##### COMPSCI ###### size 80 ###### gpa 3.5973723939811513 ###### top Share 79.43204057412802 ###### count 175.5 #### terms ##### 1232 ###### school ###### size 1216 ###### gpa 3.575457431972425 ###### top Share 77.33712216234007 ###### count 66 ###### COMPSCI ###### size 50 ###### gpa 3.522872390836507 ###### top Share 74.11555010870671 ###### count 82.5 ##### 1242 ###### school ###### size 1295 ###### gpa 3.595302892737449 ###### top Share 78.36291889885307 ###### count 67 ###### COMPSCI ###### size 52 ###### gpa 3.5206188715369935 ###### top Share 74.2043106737207 ###### count 99 ##### 1252 ###### school ###### size 1333 ###### gpa 3.619494049739118 ###### top Share 79.71442190500672 ###### count 69 ###### COMPSCI ###### size 54 ###### gpa 3.5244021201456657 ###### top Share 75.07938169795783 ###### count 101 ##### 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_fed8b1f5a10ba1f2cb2cd3d7", "name": "YIN LI", "count": 175, "terms": [ { "term": "1232", "count": 46, "sections": 1, "gpa": 3.891304347826087 }, { "term": "1242", "count": 35, "sections": 1, "gpa": 3.914285714285714 }, { "term": "1252", "count": 43, "sections": 1, "gpa": 4 }, { "term": "1262", "count": 51, "sections": 1, "gpa": 3.9901960784313726 } ] }, { "uid": "instructor_56e6fe70165f8f15a38e4b22", "name": "ABRAR AFZAL MAJEEDI", "count": 46, "terms": [ { "term": "1232", "count": 46, "sections": 1, "gpa": 3.891304347826087 } ] }, { "uid": "instructor_bbcb899185f84531e6c8a4ea", "name": "CAMERON RUGGLES", "count": 35, "terms": [ { "term": "1242", "count": 35, "sections": 1, "gpa": 3.914285714285714 } ] } ] ``` ## following None recorded.