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Einrichtungen >> Fakultät Wirtschaftsinformatik / Angewandte Informatik >> Bereich Angewandte Informatik >> Lehrstuhl für Erklärbares Maschinelles Lernen >>

  xAI-Sem-M1: Masterseminar Erklärbares Maschinelles Lernen

Dozent/in
Prof. Dr. Christian Ledig

Angaben
[S]
Rein Präsenz
2,00 SWS, Unterrichtssprache Englisch
Zeit und Ort: Mo 14:00 - 16:00, WE5/05.005

Voraussetzungen / Organisatorisches
Eligible Coures
MSc AI, MSc IRD, MSc WI, MSc CitH, MSc ISoSySc

Interest and registration
Registration via central registration or Email with name, matriculation number, degree program and completed ML-related modules to christian.ledig@uni-bamberg.de before 18.10.

Requirements:
Recommended to have passed an exam such xAI-DL-M, xAI-MML-M, KogSys-ML-M or AI-KI-B (Introduction to AI)

Inhalt
Initial Meeting: 14.10.; Second meeting 21.10. (Mandatory for participants)

VC Course For ongoing and current information see our VC Course

This is a joint seminar between FAU Erlangen-Nuremberg and University of Bamberg. The seminar will take place at Bamberg ERBA Campus and FAU Campus coupled in a hybrid setting. Students will attend in person in their respective home university. Final topic presentations will take place jointly in person with dates in Bamberg and Erlangen.

Topic: Human-in-the-Loop Machine Learning w/ focus on Healthcare

Motivation: Human-in-the-Loop Machine Learning describes processes in which humans and Machine Learning algorithms interact to solve one or more of the following: Making Machine Learning more accurate, Getting Machine Learning to the desired accuracy faster, Making humans more accurate, Making humans more efficient. Students will independently explore specific topics in the areas of machine learning and computer vision, which are then presented and discussed in class. Several potential topics will be provided but students are also encouraged to propose their own topics (after discussion with course lead).

Topics covered will include but are not limited to:
Introduction to Human-in-the-Loop Machine Learning: Active Learning Strategies, Uncertainty Sampling, Diversity Sampling, Other Strategies
Annotating Data for Machine Learning: Who are the right people to annotate your data?, Quality control for data annotation, User interfaces for data annotation
Transfer Learning and Pre-Trained Models: What are Embeddings?, What is Transfer Learning?
Adaptive Learning: Machine-Learning for aiding human annotation, Advanced Human-in-the-Loop Machine Learning

Goals In-depth knowledge of aspects of human-in-the-loop machine learning, including deeper insight into current research. A capability to work independently on application-driven projects. To use a holistic view to critically, independently and creatively identify, formulate and deal with complex issues. To create, analyse and critically evaluate different technical/architectural solutions. To integrate knowledge critically and systematically. To clearly present and discuss the conclusions as well as the knowledge and arguments that form the basis for these findings in written and spoken English. A consciousness of the ethical aspects of research and development work. The focus of the seminar will be biased towards approaches based on computer vision algorithms and medical image processing.

Format The presentations for this seminar will be conducted as block seminar. Dates of final presentations TBD.
We will meet in the beginning of the semester to discuss possible work areas and assign concrete topics to each participant. You will be provided pointers to literature and then independently familiarize yourself with the assigned topic. You will:
  • present your topic as a 20 minute presentation (+5 min questions) and
  • submit a written report of approximately 8 pages.
  • The goal is to run the seminar in English including presentations and the written report.
The presentations will be conducted as a block seminar towards the end of the semester.
The weekly hours mentioned in the module description are an optional time slot to get support, guidance and feedback on your topic (as required).

Expected workload & Grading
The time (work load) of this module is expected to be roughly as follows:
  • Attendance of seminar / presentation: 20h
  • Literature review and familiarization with topic: 25h
  • Preparation of presentation: 15h
  • Written report: 30h
The grade will be determined in equal parts based on the presentation and report. Attendance of the presentations is mandatory.

Englischsprachige Informationen:
Title:
xAI-Sem-M1: Master Seminar Explainable Machine Learning

Credits: 3

Zusätzliche Informationen
Erwartete Teilnehmerzahl: 15

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