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#41069 / #1

Seit WiSe 2022/23
(Deaktivierung beantragt zum WiSe 2022/23)

English

Quality Assessment for Machine Learning

3

Haufe, Stefan

benotet

Portfolioprüfung

Zugehörigkeit


Fakultät IV

Institut für Softwaretechnik und Theoretische Informatik

34352700 FG S-Professur Unsicherheit, inverse Modellierung und maschinelles Lernen

No information

Kontakt


MAR 4-4

Haufe, Stefan

haufe@tu-berlin.de

PORD-Nr.ModultitelLPBenotungPrüfungsformPNr. (POS)Modulprüfung PORDModulprüfung PNr.
44234

Learning Outcomes

Knowledge: Students will be familiar with the requirements imposed on AI systems by current and prospective regulatory frameworks. They will know different quality dimensions of AI systems and will have elaborated procedures and metrics for measuring quality along these dimensions and technical means for assuring high quality. Skills: Students will acquire practical skills to debug data science applications by analyzing data and/or code. In addition, students will also acquire or refine skills to independently review and systematically structure the literature of a well circumscribed field in order to address a given set of questions, and will gain experience in presenting the outcome to a critical audience as well as in participating in scientific discussions. Competencies: Students will be able to analyze the the risks and failure modes of a given AI system or product. They will be able to suggest procedures and tests to benchmark whether a given system/product will function as intended in a variety of settings and suggest ways to overcome unwanted system behavior. Students will also be able to discuss ethical, economic and other implications of failure modes of AI systems.

Content

- motivating examples and use cases from the medical, automotive and finance domains - taxonomy of use cases, associated risks and failure modes - legal approaches to regulate AI systems for critical applications - ethical considerations - quality dimensions for AI/ML systems  * data quality  * model performance, generalization  * model robustness  * model fairness  * transparency   * uncertainty calibration  * model interpretation and types of explainability  * data privacy - quantitative metrics and test to measure quality - practical approaches to ensure/improve quality - simulations and benchmarks - current standardization efforts

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Quality Assurance for Machine LearningSEMWSEnglish2

Workload and Credit Points

Quality Assurance for Machine Learning (SEM):

Workload descriptionMultiplierHoursTotal
90.0h(~3 LP)
Attendance15.02.0h30.0h
Pre/post processing15.04.0h60.0h
The Workload of the module sums up to 90.0 Hours. Therefore the module contains 3 Credits.

Description of Teaching and Learning Methods

The module currently consists of a seminar. Students will prepare a presentation to a specific topic based on a provided collection of published material. Student presentations will be framed by short lecture segments introducing, contextualizing and connecting the presented topics. Each course slot will contain discussion periods, in which active participation is fostered. Students will also conduct a group work to analyze a given AI system, and present their results to the seminar audience. Moreover, students will complete a homework.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

A BSc degree in Computer Science is recommended. Successful completion of an introductory module on ML such as "Machine Learning I" or "Machine Intelligence I" is recommended. Programming skills in at least one language (e.g., Matlab, Python, R) are required.

Mandatory requirements for the module test application:

No information

Module completion

Grading

graded

Type of exam

Portfolio examination

Type of portfolio examination

100 points in total

Language

English

Test elements

NamePointsCategorieDuration/Extent
(Deliverable assessment) Presentation50oral30 min
(Deliverable assessment) Group work25oral60 min
(Deliverable assessment) Homework25practical3 pages

Grading scale

1.01.31.72.02.32.73.03.33.74.0
86.082.078.074.070.066.062.058.054.050.0

Test description (Module completion)

The module grade is calculated based on 1. The quality of a paper presentation (50%). 2. The quality of the presentation of a group work (25%). 3. The quality of a homework (25%).

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:
Wintersemester.

Maximum Number of Participants

The maximum capacity of students is 25.

Registration Procedures

Students can sign up for the course in MOSES.

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable

 

Electronical lecture notes

Availability:  unavailable

 

Literature

Recommended literature
No recommended literature given

Assigned Degree Programs


This module is used in the following Degree Programs (new System):

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Computer Engineering (M. Sc.)14WiSe 2022/23WiSe 2022/23
Computer Science (Informatik) (M. Sc.)13WiSe 2022/23WiSe 2022/23
Elektrotechnik (M. Sc.)12WiSe 2022/23WiSe 2022/23

Miscellaneous

No information