# AAE 722: Machine Learning in Applied Economic Analysis | UW–Madison [View on UW Courses](https://uwcourses.com/courses/AAE_722) ## 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 ee89a9fdf9bd5d80d397bf3815e9659a8cedee8b8b0160d2178ab6206495f13c ### course id AAE 722 ### course uid course\_02588cfacd3ecfec7b3ff64b ### catalog version id 8d227b9155d80a9e5c59ef35a09fc4ea0d60efde01dd495557126dc90f751d85 ### course number 722 ### subjects * AAE ### title MACHINE LEARNING IN APPLIED ECONOMIC ANALYSIS ### description The basic methods, implementation and applications of machine learning for understanding contemporary economic issues using large data sets. Building upon understanding of standard econometric models, the topics include data mining techniques; regression model selection and regularization; post selection inference and economic applications; tree-based methods; neural networks; random forests and casual inference; and unsupervised learning. ### requirements text A A E 636orECON 704 ### credit offering ids None recorded. ### llm job id enrich-8b774950c2b6adfdc46d1b82 ### llm output id 7856a65d04241a018cc26266f284664130a0d2c3bcca9536477f3e9e0654d872 ### llm model nvidia/Qwen3.6-35B-A3B-NVFP4 ### llm model revision 1355db6a052410cfd62085d94b58866fd0f2c3c5 ### llm task version 14 ### llm search status valid ### llm summary Teaches machine learning methods and applications for analyzing economic issues with large datasets. ### llm topics * Data mining, regression, neural networks, random forests, causal inference, unsupervised learning ### llm skills * Machine learning techniques for economic data * Applying ML to economic issues ### llm assumed background * Standard econometric models and regression analysis * Calculus and linear algebra ### llm search phrases * machine learning economics * econometric machine learning * applied economic analysis ML * regression regularization economics ### llm requirements status valid ### llm student summary status valid ### llm experience status insufficient\_evidence ### catalog variants None recorded. ### student summary #### context hash 684699101887178d4a43a30d187d59b161c34442f2bf25739754f05ba703ef7c #### course id AAE 722 #### 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 672f506f2fc2f46a071b9777f4a92cc197b2ccdeef25590e9285146d8c7e7f90 #### quick take Recent recorded grades — Fall 2023: 3.60 GPA, 75.0% A/AB (n=20 letter grades); Fall 2024: 3.86 GPA, 90.9% A/AB (n=11 letter grades); Fall 2025: 3.92 GPA, 94.7% A/AB (n=19 letter grades). ```json { "citations": [ { "course_id": "AAE 722", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "51088ce4-3125-3d0a-aa82-a6ecea46f644", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1242", "type": "grade" }, { "course_id": "AAE 722", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "51088ce4-3125-3d0a-aa82-a6ecea46f644", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1252", "type": "grade" }, { "course_id": "AAE 722", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "51088ce4-3125-3d0a-aa82-a6ecea46f644", "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 None recorded. #### term id 1272 #### term name 2026 Fall #### version 2 ### requirements #### nodes ```json [ { "children": [ "n1", "n2" ], "condition": null, "course": null, "evidence": "A A E 636orECON 704", "id": "n0", "kind": "any" }, { "children": [], "condition": null, "course": { "course_number": 636, "minimum_grade": null, "subjects": [ "AAE" ], "timing": "prior" }, "evidence": "A A E 636", "id": "n1", "kind": "course" }, { "children": [], "condition": null, "course": { "course_number": 704, "minimum_grade": null, "subjects": [ "ECON" ], "timing": "prior" }, "evidence": "ECON 704", "id": "n2", "kind": "course" } ] ``` #### notes None recorded. #### root n0 #### status parsed ### instructors None recorded. ### offerings None recorded. ### sections None recorded. ### grade instructors ```json [ { "instructor_uid": "instructor_1a6bc3cb256dd51406ba0df4", "source": "madgrades", "source_instructor_id": "3112273", "identity_basis": "source_id", "identity_status": "source_identified", "name": "XIAODONG DU", "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": 3.281, "graded": 967, "counts": [ 423, 185, 134, 99, 68, 48, 10 ], "sections": 80 }, "instructor_url": "/instructors/XIAODONG_DU" }, { "instructor_uid": "instructor_5f8af0ad1aaf59440a9b722b", "source": "madgrades", "source_instructor_id": "3967736", "identity_basis": "source_id", "identity_status": "source_identified", "name": "NICHOLAS GALLAGHER", "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": 3.6, "graded": 20, "counts": [ 10, 5, 4, 1, 0, 0, 0 ], "sections": 1 }, "instructor_url": "/instructors/NICHOLAS_GALLAGHER--instructor_5f8af0ad1aaf59440a9b722b" }, { "instructor_uid": "instructor_000906a0de605f58dfb66b9c", "source": "madgrades", "source_instructor_id": "4725330", "identity_basis": "source_id", "identity_status": "source_identified", "name": "JING YI", "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": 3.87, "graded": 162, "counts": [ 132, 22, 5, 2, 1, 0, 0 ], "sections": 17 }, "instructor_url": "/instructors/JING_YI--instructor_000906a0de605f58dfb66b9c" } ] ``` ### grades ```json [ { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "AAE 722", "course_uid": "course_02588cfacd3ecfec7b3ff64b", "term_id": "1232", "term_name": "Fall 2022", "instructors": [ "XIAODONG DU" ], "a": 5, "ab": 5, "b": 2, "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": 12, "source_aliases": [ "AAE 722" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "AAE 722", "course_uid": "course_02588cfacd3ecfec7b3ff64b", "term_id": "1242", "term_name": "Fall 2023", "instructors": [ "NICHOLAS GALLAGHER", "XIAODONG DU" ], "a": 10, "ab": 5, "b": 4, "bc": 1, "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": 20, "source_aliases": [ "AAE 722" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "AAE 722", "course_uid": "course_02588cfacd3ecfec7b3ff64b", "term_id": "1252", "term_name": "Fall 2024", "instructors": [ "Jing Yi" ], "a": 9, "ab": 1, "b": 1, "bc": 0, "c": 0, "d": 0, "f": 0, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 1, "no_work": 0, "not_reported": 0, "other": 0, "total": 12, "source_aliases": [ "AAE 722" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "AAE 722", "course_uid": "course_02588cfacd3ecfec7b3ff64b", "term_id": "1262", "term_name": "Fall 2025", "instructors": [ "Jing Yi" ], "a": 18, "ab": 0, "b": 0, "bc": 1, "c": 0, "d": 0, "f": 0, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 1, "no_work": 0, "not_reported": 0, "other": 0, "total": 20, "source_aliases": [ "AAE 722" ] } ] ``` ### statistics #### gpa 3.75 #### graded 62 #### counts * 42 * 11 * 7 * 2 * 0 * 0 * 0 ### grade conflicts None recorded. ### evidence #### history * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course\_02588cfacd3ecfec7b3ff64b-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course_02588cfacd3ecfec7b3ff64b-0.json) #### traces * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course\_02588cfacd3ecfec7b3ff64b-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course_02588cfacd3ecfec7b3ff64b-0.json) #### results * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course\_02588cfacd3ecfec7b3ff64b-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course_02588cfacd3ecfec7b3ff64b-0.json) ### revision e243353dcb7d79b7247ced91d69443ef4c2a6349 ## context ### all #### term #### gpa 3.75 #### count 62 #### university ##### size 2734 ##### gpa Percentile 50 ##### count Percentile 29 ##### 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": "AAE", "comparison": { "size": 23, "gpaPercentile": 86, "countPercentile": 27, "medianCount": 97, "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": 2, "current": false }, { "range": "3.2–3.6", "count": 12, "current": false }, { "range": "3.6–4.0", "count": 9, "current": true } ] } } ] ``` ### terms #### 1232 ##### term 1232 ##### gpa 3.625 ##### count 12 ##### departments | subject | comparison | | ------- | ---------- | | AAE | | #### 1242 ##### term 1242 ##### gpa 3.6 ##### count 20 ##### departments | subject | comparison | | ------- | ---------- | | AAE | | #### 1252 ##### term 1252 ##### gpa 3.8636363636363638 ##### count 11 ##### departments | subject | comparison | | ------- | ---------- | | AAE | | #### 1262 ##### term 1262 ##### gpa 3.9210526315789473 ##### count 19 ##### departments | subject | comparison | | ------- | ---------- | | AAE | | ### benchmarks #### all ##### school ###### size 2734 ###### gpa 3.6828436876066126 ###### top Share 83.37648184927653 ###### count 105 ##### AAE ###### size 23 ###### gpa 3.5106229616951588 ###### top Share 72.90449271115054 ###### count 97 #### terms ##### 1232 ###### school ###### size 1216 ###### gpa 3.575457431972425 ###### top Share 77.33712216234007 ###### count 66 ###### AAE ###### size 11 ###### gpa 3.4135482380136697 ###### top Share 64.80379498403573 ###### count 45 ##### 1242 ###### school ###### size 1295 ###### gpa 3.595302892737449 ###### top Share 78.36291889885307 ###### count 67 ##### 1252 ###### school ###### size 1333 ###### gpa 3.619494049739118 ###### top Share 79.71442190500672 ###### count 69 ##### 1262 ###### school ###### size 1320 ###### gpa 3.628825763035853 ###### top Share 80.21118846327654 ###### count 70 ## instructor Trends ```json [ { "uid": "instructor_1a6bc3cb256dd51406ba0df4", "name": "XIAODONG DU", "count": 32, "terms": [ { "term": "1232", "count": 12, "sections": 1, "gpa": 3.625 }, { "term": "1242", "count": 20, "sections": 1, "gpa": 3.6 } ] }, { "uid": "instructor_000906a0de605f58dfb66b9c", "name": "JING YI", "count": 30, "terms": [ { "term": "1252", "count": 11, "sections": 1, "gpa": 3.8636363636363638 }, { "term": "1262", "count": 19, "sections": 1, "gpa": 3.9210526315789473 } ] }, { "uid": "instructor_5f8af0ad1aaf59440a9b722b", "name": "NICHOLAS GALLAGHER", "count": 20, "terms": [ { "term": "1242", "count": 20, "sections": 1, "gpa": 3.6 } ] } ] ``` ## following None recorded.