# MATH 345: Linear Algebra and Optimization | UW–Madison [View on UW Courses](https://uwcourses.com/courses/MATH_345) ## 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 6d9afdb9f65c677a2f1621ae528d4a2eeedb9599ab81a8e4d157289adec924fb ### course id MATH 345 ### course uid course\_2e0053b739243eda32fb0fc2 ### catalog version id e103277e62ecd6b7571540b5f0d260b40c105e49f852fbd6fbbb5cedd0beb12c ### course number 345 ### subjects * MATH ### title LINEAR ALGEBRA AND OPTIMIZATION ### description Introduction to linear algebra, differential calculus in several variables, and basic optimization theory with applications to data science and related topics. Vectors, analytic geometry, matrices, linear functions, linear independence, orthogonality, inverses, partial derivatives and gradients, Taylor approximation, gradient descent, Lagrange multipliers, clustering, regression, classification. Implementation in Python. ### requirements text MATH 222and (COMP SCI 200,220,300,310,320, or placement inCOMP SCI 300). Not open to students with credit forMATH 320,340,341, or375. ### credits min 4 ### credits max 4 ### credit offering ids * 1272:600:026960 ### llm job id enrich-8b774950c2b6adfdc46d1b82 ### llm output id d29131fb168030ba50d41570fde40efcc739abb2aa654f28cb21d3c03cb3d59c ### llm model nvidia/Qwen3.6-35B-A3B-NVFP4 ### llm model revision 1355db6a052410cfd62085d94b58866fd0f2c3c5 ### llm task version 14 ### llm search status valid ### llm summary MATH 345 introduces linear algebra, multivariable calculus, and optimization theory with data science applications, implemented in Python. ### llm topics * Vectors, analytic geometry, matrices, linear functions, independence, orthogonality, and inverses. * Partial derivatives, gradients, Taylor approximation, gradient descent, and Lagrange multipliers. * Clustering, regression, and classification. ### llm skills * Python implementation of mathematical concepts. * Linear algebra and differential calculus in several variables. * Clustering, regression, and classification techniques. * Partial derivatives, gradients, Taylor approximation, gradient descent, and Lagrange multipliers. ### llm assumed background * Calculus and analytic geometry, specifically vector geometry and series. * Fundamental programming concepts and problem decomposition. * Data science programming using Python. * Object-oriented programming and data structures. * Computer and analytical problem-solving skills. * Intermediate data science programming, graphs, and optimization techniques. ### llm search phrases * linear algebra optimization data science * gradient descent Lagrange multipliers Python * MATH 345 prerequisites * vector geometry calculus optimization ### llm requirements status valid ### llm student summary status valid ### llm experience status insufficient\_evidence ### catalog variants None recorded. ### student summary #### context hash 863200a0f388876510f11225af990c1860e95c2555687ddefb904782cb9fe158 #### course id MATH 345 #### current instructors ```json [ { "instructor_uid": "instructor_504a40032538376afa9c2473", "message": "No course-specific reviews available", "name": "Sebastien Roch", "review_status": "no_course_reviews", "rmp_instructor_id": "rmp:1781624", "summary": [ { "citations": [ { "course_id": "MATH 345", "run_id": "20260907T155543-ce3781c4", "section_number": 1, "source_course_id": "140f55cd-b9a4-37d3-becd-9f4e3dc07cbd", "source_record": { "entity_id": "140f55cd-b9a4-37d3-becd-9f4e3dc07cbd", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1254", "type": "grade" } ], "text": "Recent recorded grades — Spring 2025: 3.12 GPA, 52.9% A/AB (n=17 letter grades)." } ] } ] ``` #### 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 — Spring 2025: 3.12 GPA, 52.9% A/AB (n=17 letter grades); Spring 2026: 2.98 GPA, 38.5% A/AB (n=26 letter grades). ```json { "citations": [ { "course_id": "MATH 345", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "140f55cd-b9a4-37d3-becd-9f4e3dc07cbd", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1254", "type": "grade" }, { "course_id": "MATH 345", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "140f55cd-b9a4-37d3-becd-9f4e3dc07cbd", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1264", "type": "grade" } ] } ``` #### student experience None recorded. #### task hash 74fb0997943e888960bbc9e47db8c4fd12e4292d55c20509d8d89db6a9910f68 #### teaching history SEBASTIEN ROCH is recorded teaching in Spring 2025. 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"quality_count": 12, "difficulty_count": 12, "profile_id": "rmp:1781624", "source_url": "https://www.ratemyprofessors.com/professor/1781624", "match_basis": "exact_name", "observed_at": "2026-09-07 16:06:27.124670+00:00", "courses": { "course_85a2851be919e3db46f0fbc1": { "review_count": 5, "quality": 2.8, "difficulty": 3.2, "quality_count": 5, "difficulty_count": 5 }, "course_c602622d732dbbc9aceeeddf": { "review_count": 2, "quality": 4, "difficulty": 4, "quality_count": 2, "difficulty_count": 2 }, "course_b533f0f6b5fef2c11c4fa06c": { "review_count": 1, "quality": 2, "difficulty": 5, "quality_count": 1, "difficulty_count": 1 }, "course_6c15cc3b45a4c19a3ad6a2cd": { "review_count": 4, "quality": 1, "difficulty": 5, "quality_count": 4, "difficulty_count": 4 } }, "bayesian_quality": 3.1610536147166224, "prior_mean": 3.6596857835465957, "prior_weight": 20 }, "instructor_url": "/instructors/SEBASTIEN_ROCH" } ] ``` ### grades ```json [ { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "MATH 345", "course_uid": "course_2e0053b739243eda32fb0fc2", "term_id": "1254", "term_name": "Spring 2025", "instructors": [ "Sebastien Roch" ], "a": 6, "ab": 3, "b": 3, "bc": 3, "c": 0, "d": 2, "f": 0, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 17, "source_aliases": [ "MATH 345" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "MATH 345", "course_uid": "course_2e0053b739243eda32fb0fc2", "term_id": "1264", "term_name": "Spring 2026", "instructors": [ "SARAH STRIKWERDA" ], "a": 5, "ab": 5, "b": 8, "bc": 2, "c": 5, "d": 1, "f": 0, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 26, "source_aliases": [ "MATH 345" ] } ] ``` ### statistics #### gpa 3.035 #### graded 43 #### counts * 11 * 8 * 11 * 5 * 5 * 3 * 0 ### grade conflicts None recorded. ### evidence #### history * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course\_2e0053b739243eda32fb0fc2-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course_2e0053b739243eda32fb0fc2-0.json) #### traces * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course\_2e0053b739243eda32fb0fc2-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course_2e0053b739243eda32fb0fc2-0.json) #### meetings * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/meetings/course\_2e0053b739243eda32fb0fc2-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/meetings/course_2e0053b739243eda32fb0fc2-0.json) #### results * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course\_2e0053b739243eda32fb0fc2-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course_2e0053b739243eda32fb0fc2-0.json) ### revision e243353dcb7d79b7247ced91d69443ef4c2a6349 ## context ### all #### term #### gpa 3.0348837209302326 #### count 43 #### university ##### size 2021 ##### gpa Percentile 4 ##### count Percentile 20 ##### median Count 77 ##### 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 | 21 | false | | 2.8–3.2 | 152 | true | | 3.2–3.6 | 521 | false | | 3.6–4.0 | 1327 | false | #### departments ```json [ { "subject": "MATH", "comparison": { "size": 65, "gpaPercentile": 17, "countPercentile": 27, "medianCount": 84, "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": 8, "current": false }, { "range": "2.8–3.2", "count": 20, "current": true }, { "range": "3.2–3.6", "count": 23, "current": false }, { "range": "3.6–4.0", "count": 14, "current": false } ] } } ] ``` ### terms #### 1254 ##### term 1254 ##### gpa 3.1176470588235294 ##### count 17 ##### departments | subject | comparison | | ------- | ---------- | | MATH | | #### 1264 ##### term 1264 ##### gpa 2.980769230769231 ##### count 26 ##### departments | subject | comparison | | ------- | ---------- | | MATH | | ### benchmarks #### all ##### school ###### size 2021 ###### gpa 3.660373947354374 ###### top Share 82.25902967226666 ###### count 77 ##### MATH ###### size 65 ###### gpa 3.312424790878224 ###### top Share 62.03685043120841 ###### count 84 #### terms ##### 1254 ###### school ###### size 1289 ###### gpa 3.6126423469389106 ###### top Share 79.48050408754871 ###### count 66 ###### MATH ###### size 39 ###### gpa 3.180435541912479 ###### top Share 53.878371816747475 ###### count 89 ##### 1264 ###### school ###### size 1283 ###### gpa 3.6229729166451925 ###### top Share 80.13669149396227 ###### count 66 ###### MATH ###### size 42 ###### gpa 3.13598176627681 ###### top Share 52.25969905887213 ###### count 80 ## instructor Trends ```json [ { "uid": "instructor_f795ac1b255f155ffee70bb2", "name": "SARAH STRIKWERDA", "count": 26, "terms": [ { "term": "1264", "count": 26, "sections": 1, "gpa": 2.980769230769231 } ] }, { "uid": "instructor_a19b17502e85c5e433a5c53f", "name": "SEBASTIEN ROCH", "count": 17, "terms": [ { "term": "1254", "count": 17, "sections": 1, "gpa": 3.1176470588235294 } ] } ] ``` ## following | code | title | | ---------------------- | ----------------------------------------------------------------------- | | ASTRON 590 | ASTRODYNAMICS | | BMI/COMPSCI 567 | BIOMEDICAL IMAGE ANALYSIS | | COMPSCI 540 | INTRODUCTION TO ARTIFICIAL INTELLIGENCE | | COMPSCI 541 | THEORY & ALGORITHMS FOR DATA SCIENCE | | COMPSCI 550 | ALGORITHMIC GAME THEORY & LEARNING | | COMPSCI 557 | PARALLEL & THROUGHPUT- OPTIMIZED PROGRAMMING | | COMPSCI 566 | INTRODUCTION TO COMPUTER VISION | | COMPSCI 580 | INTELLIGENT ROBOTICS | | COMPSCI/ECE/MATH 435 | INTRODUCTION TO CRYPTOGRAPHY | | COMPSCI/MATH 513 | NUMERICAL LINEAR ALGEBRA | | COMPSCI/MATH 514 | NUMERICAL ANALYSIS | | COMPSCI/MATH/STAT 475 | INTRODUCTION TO COMBINATORICS | | ISYE/MATH/OTM/STAT 632 | INTRODUCTION TO STOCHASTIC PROCESSES | | MATH 321 | APPLIED MATHEMATICAL ANALYSIS 1: VECTOR AND COMPLEX CALCULUS | | MATH 415 | APPLIED DYNAMICAL SYSTEMS, CHAOS AND MODELING | | MATH 443 | APPLIED LINEAR ALGEBRA | | MATH 444 | GRAPHS AND NETWORKS IN DATA SCIENCE | | MATH 467 | INTRODUCTION TO NUMBER THEORY | | MATH 519 | ORDINARY DIFFERENTIAL EQUATIONS | | MATH 522 | ANALYSIS II | | MATH 535 | MATHEMATICAL METHODS IN DATA SCIENCE | | MATH 540 | LINEAR ALGEBRA II | | MATH 541 | MODERN ALGEBRA 1 | | MATH 561 | DIFFERENTIAL GEOMETRY | | MATH 616 | DATA-DRIVEN DYNAMICAL SYSTEMS, STOCHASTIC MODELING AND PREDICTION | | MATH/PHILOS 571 | MATHEMATICAL LOGIC | | STAT 433 | DATA SCIENCE WITH R | | STAT 451 | INTRODUCTION TO MACHINE LEARNING AND STATISTICAL PATTERN CLASSIFICATION | | STAT 453 | INTRODUCTION TO DEEP LEARNING AND GENERATIVE MODELS | | STAT 456 | APPLIED MULTIVARIATE ANALYSIS | | STAT 575 | STATISTICAL METHODS FOR SPATIAL DATA |