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Vorlesungsverzeichnis >> Fakultät Wirtschaftsinformatik und Angewandte Informatik >>

  xAI-Sem-M1: Masterseminar Erklärbares Maschinelles Lernen (xAI-sem-M1)

Dozent/in
Prof. Dr. Christian Ledig

Angaben
Seminar
Rein Präsenz
2 SWS, benoteter Schein
Zeit und Ort: n.V.; Bemerkung zu Zeit und Ort: Wir streben an, diese Veranstaltung in Präsenz durchzuführen. First meeting October 20, 4pm ct, WE5/05.003 // Second meeting October 24, 2pm ct, WE5/02.005

Voraussetzungen / Organisatorisches
Interest and registration
If you have questions or want to express interest, please send an Email with name and matriculation number to christian.ledig@uni-bamberg.de. Registration via central VC course

Requirements:
completed course "Lernende System / Machine Learning" or "Einführung in die KI / Introduction into AI"

Inhalt
This is a joint seminar between Prof. Kainz (FAU Erlangen-Nuremberg) and Prof. Ledig (University of Bamberg). The seminar will take place at Bamberg ERBA Campus and FAU Campus. Initial topic selection will take place in a hybrid format in Bamberg/Erlangen (in person on each site). Final topic presentations will take place in two sessions, one in person in Bamberg, one in person in 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 Aim of this seminar is to give students insights about state-of-the-art Active Learning and interactive data analysis methods. Students will independently explore specific topics, 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 presentationsTBD.
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: Masterseminar Explainable Machine Learning

Credits: 3

Zusätzliche Informationen
Erwartete Teilnehmerzahl: 15

Institution: Lehrstuhl für Erklärbares Maschinelles Lernen

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