# BMI/STAT 828: Semiparametric Methods in Data Science | UW–Madison

[View on UW Courses](https://uwcourses.com/courses/BMI_STAT_828)

## 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

4563d2e51bc429bc259c7922526a986d35d84373221aae31241118fd05141c81

### course id

BMI/STAT 828

### course uid

course\_050cac70b4ff0bdca35ecad4

### catalog version id

0051dd7a0230434a65c15b1f6c0607c02004cd33deafd77d2abd951727e3a68f

### course number

828

### subjects

* BMI
* STAT

### title

SEMIPARAMETRIC METHODS IN DATA SCIENCE

### description

Review of statistical convergence modes, M-estimation, and basics of Hilbert space. Introduction of how to derive the nuisance tangent space, its complement, and the corresponding efficient influence function, from the geometric perspective of semiparametric models. Introduction of how to estimate nuisance functions using machine learning methods, and their implementations in R and/or Python. Introduction of a variety of semiparametric models in missing data analysis, causal inference, dimension reduction, precision medicine, semi-supervised learning, transfer learning and domain adaptation.

### requirements text

Graduate/professional standing

### credit offering ids

None recorded.

### llm job id

enrich-8b774950c2b6adfdc46d1b82

### llm output id

ab1573131b779da98e62b7a8e3712ba1381bf541e02b6cee225cd61342b74323

### llm model

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

### llm model revision

1355db6a052410cfd62085d94b58866fd0f2c3c5

### llm task version

14

### llm search status

valid

### llm summary

Covers semiparametric methods, including deriving efficient influence functions and applying machine learning to causal inference and missing data.

### llm topics

* Geometric perspective of semiparametric models
* Nuisance tangent spaces and efficient influence functions
* Applications in causal inference, missing data, and transfer learning

### llm skills

* Deriving nuisance tangent spaces and efficient influence functions
* Estimating nuisance functions with machine learning in R/Python
* Applying semiparametric models to causal inference and data science

### llm assumed background

* Statistical convergence, M-estimation, and Hilbert space fundamentals

### llm search phrases

* semiparametric models
* nuisance tangent space
* efficient influence function
* machine learning estimation
* causal inference
* missing data analysis

### llm requirements status

valid

### llm student summary status

valid

### llm experience status

insufficient\_evidence

### catalog variants

None recorded.

### student summary

#### context hash

4bb0268ab92f4010bfa1193797a9779ac96f186217228595de8d86dda7bab5a5

#### course id

BMI/STAT 828

#### 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

Recent recorded grades — Fall 2024: 4.00 GPA, 100.0% A/AB (n=14 letter grades).

```json
{
  "citations": [
    {
      "course_id": "BMI/STAT 828",
      "run_id": "20260907T155543-ce3781c4",
      "source_record": {
        "entity_id": "bccdba17-5a9a-359e-9e73-185041f3950e",
        "file": "tables/observations.parquet",
        "kind": "grades",
        "source": "madgrades"
      },
      "table": "grades_latest",
      "term_id": "1252",
      "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": [],
    "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

```json
[
  {
    "instructor_uid": "instructor_57424157730da96ec177f067",
    "source": "madgrades",
    "source_instructor_id": "4286103",
    "identity_basis": "source_id",
    "identity_status": "source_identified",
    "name": "JIWEI ZHAO",
    "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": 3.918,
      "graded": 116,
      "counts": [
        106,
        7,
        2,
        0,
        0,
        0,
        1
      ],
      "sections": 19
    },
    "instructor_url": "/instructors/JIWEI_ZHAO--instructor_57424157730da96ec177f067"
  }
]
```

### grades

```json
[
  {
    "run_id": "20260907T155543-ce3781c4",
    "semester": "1272",
    "observed_at": "2026-09-07 15:55:43.033547+00:00",
    "course_id": "BMI/STAT 828",
    "course_uid": "course_050cac70b4ff0bdca35ecad4",
    "term_id": "1252",
    "term_name": "Fall 2024",
    "instructors": [
      "Jiwei Zhao"
    ],
    "a": 14,
    "ab": 0,
    "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": 14,
    "source_aliases": [
      "BMI/STAT 828"
    ]
  }
]
```

### statistics

#### gpa

4

#### graded

14

#### counts

* 14
* 0
* 0
* 0
* 0
* 0
* 0

### grade conflicts

None recorded.

### evidence

#### history

* [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course\_050cac70b4ff0bdca35ecad4-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course_050cac70b4ff0bdca35ecad4-0.json)

#### traces

* [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course\_050cac70b4ff0bdca35ecad4-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course_050cac70b4ff0bdca35ecad4-0.json)

#### results

* [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course\_050cac70b4ff0bdca35ecad4-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course_050cac70b4ff0bdca35ecad4-0.json)

### revision

e243353dcb7d79b7247ced91d69443ef4c2a6349

## context

### all

#### term



#### gpa

4

#### count

14

#### departments

| subject | comparison |
| ------- | ---------- |
| BMI     |            |
| STAT    |            |

### terms

#### 1252

##### term

1252

##### gpa

4

##### count

14

##### departments

| subject | comparison |
| ------- | ---------- |
| BMI     |            |
| STAT    |            |

### benchmarks

#### all

##### school

###### size

1333

###### gpa

3.619494049739118

###### top Share

79.71442190500672

###### count

69

##### STAT

###### size

32

###### gpa

3.423918594183889

###### top Share

68.17668754351217

###### count

70.5

#### terms

##### 1252

###### school

###### size

1333

###### gpa

3.619494049739118

###### top Share

79.71442190500672

###### count

69

###### STAT

###### size

32

###### gpa

3.423918594183889

###### top Share

68.17668754351217

###### count

70.5

## instructor Trends

```json
[
  {
    "uid": "instructor_57424157730da96ec177f067",
    "name": "JIWEI ZHAO",
    "count": 14,
    "terms": [
      {
        "term": "1252",
        "count": 14,
        "sections": 1,
        "gpa": 4
      }
    ]
  }
]
```

## following

None recorded.