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Einrichtungen >> Fakultät Wirtschaftsinformatik / Angewandte Informatik >> Bereich Angewandte Informatik >>

Lehrstuhl für Erklärbares Maschinelles Lernen

 

xAI - Master- und Doktorandenseminar [xAI-MDSem]

Dozent/in:
Christian Ledig
Angaben:
Seminar, 2 SWS
Termine:
Do, 15:00 - 17:00, Online-Meeting
Voraussetzungen / Organisatorisches:
zur Voranmeldung und bei Interesse bitte email an christian.ledig@uni-bamberg.de.
Zielgruppe: Studierende im Master mit laufenden oder Interesse an zukünftigen Masterarbeiten am Lehrstuhl. Studierende mit generellem Interesse an aktuellen Forschungsfortschritten.
Inhalt:
Forum zur Diskussion von laufenden und zukünftigen Master- bzw. Promotionsthemen, sowie Forschungsprojekten und Forschungstrends im internationalen Umfeld. Möglichkeit zum Austausch und Networking mit Studierenden der FAU Erlangen-Nürnberg und dem Imperial College London.

 

xAI-DL-M: Deep Learning

Dozent/in:
Christian Ledig
Angaben:
Vorlesung, 2,00 SWS, ECTS: 6
Termine:
Di, 8:00 - 10:00, WE5/00.019
Voraussetzungen / Organisatorisches:
MSc AI, MSc WI, MSc CitH, MSc ISoSySc
Sign up: VC Course
Inhalt:
Vorkenntnisse / Prerequisites:
Good working knowledge of programming (e.g., in Python); Recommended completion of modules: Mathematics for Machine Learning (xAI-MML), Lernende System / Machine Learning [KogSys-ML-M], Einführung in die Künstliche Intelligenz / Introduction to AI [AI-KI-B], Mathematik für Informatik 2 (Lineare Algebra) [KTR-MfI-2], Algorithmen und Datenstrukturen [AI-AuD-B]

Description:
Deep Learning is a form of machine learning that learns hierarchical concepts and representations directly from data. Enabled by continuously growing dataset sizes, compute power and rapidly evolving open-source frameworks Deep Learning based AI systems continue to set the state of the art in many applications and industries. The course will provide an introduction to the most relevant techniques in the field of Deep Learning and a broad range of its applications.
The lecture will be held in English. The following is a selection of topics that will be addressed in the course:
  • Relevant concepts in linear algebra, probability and information theory
  • Deep feedforward networks
  • Convolutional Neural Networks
  • Regularization, Batch Normalization
  • Optimization (Backpropagation, Stochastic Gradient Decent) and Cost Functions
  • Classification (binary, multiclass, multilabel)
  • Object Detection & Segmentation
  • Generative Modelling
  • Attention mechanisms & Transformer Networks
  • Evaluation of ML approaches

Goals:
In this course students will learn/recap some fundamentals from mathematics and machine learning that are critical for the introduction of the concept of Deep Learning. Participants will learn about various foundational technical aspects including optimization and regularization strategies, cost functions and important network architectures such as Convolutional Networks. Students will further get an insight into more advanced concepts such as sequence modelling and generative modelling. Participants will further learn about representative architectures of important algorithm categories, e.g., classification, detection, segmentation, some of their concrete use cases and how to evaluate them.
The lecture is accompanied by exercises and assignments that will help participants develop practical, hands-on experience. In those exercises students will learn how to implement and evaluate Deep Learning algorithms using Python and its respective commonly used libraries.
Empfohlene Literatur:
  • Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep Learning, MIT Press, 2016
  • Zhang, Lipton, et al.: Dive into Deep Learning (https://d2l.ai/)

Further literature will be announced at the beginning of the course.

 

xAI-DL-M: Deep Learning, Gruppe 1

Dozent/in:
Sebastian Dörrich
Angaben:
Übung, 2,00 SWS
Termine:
Mi, 10:00 - 12:00, WE5/01.003

 

xAI-DL-M: Deep Learning, Gruppe 2

Dozent/in:
Sebastian Dörrich
Angaben:
Übung, 2,00 SWS
Termine:
Mi, 12:00 - 14:00, WE5/05.005

 

xAI-DL-M: Deep Learning, Gruppe 3

Dozent/in:
Francesco Di Salvo
Angaben:
Übung, 2,00 SWS
Termine:
Do, 16:00 - 18:00, WE5/05.005

 

xAI-Proj-B: Bachelorprojekt Erklärbares Maschinelles Lernen

Dozentinnen/Dozenten:
Ines Rieger, Christian Ledig
Angaben:
Übung, 4,00 SWS, ECTS: 6
Termine:
Mi, 14:00 - 18:00, WE5/04.003

 

xAI-Proj-M: Masterprojekt Erklärbares Maschinelles Lernen

Dozentinnen/Dozenten:
Francesco Di Salvo, Christian Ledig
Angaben:
Übung, 4,00 SWS, ECTS: 6
Termine:
Di, 14:00 - 18:00, WE5/04.003
Initial Meeting on October 17th - 17/10/23- (Q&A and preliminary overview- not mandatory), Kick-off: 24/10/23 (mandatory for participants)

 

xAI-Sem-B1: Bachelorseminar Erklärbares Maschinelles Lernen

Dozentinnen/Dozenten:
Sebastian Dörrich, Christian Ledig
Angaben:
Seminar, 2,00 SWS, ECTS: 3
Termine:
Mo, 14:00 - 16:00, WE5/05.005

 

xAI-Sem-M1: Masterseminar Erklärbares Maschinelles Lernen

Dozent/in:
Christian Ledig
Angaben:
Seminar, 2,00 SWS, ECTS: 3
Termine:
Mo, 16:00 - 18:00, WE5/05.005
Voraussetzungen / Organisatorisches:
Interest and registration
Email with name, matriculation number, degree program and completed ML-related modules to christian.ledig@uni-bamberg.de before 18.10.

Requirements:
Successfully passed an exam such xAI-DL-M, xAI-MML-M, KogSys-ML-M or AI-KI-B (Introduction to AI)
Inhalt:
Initial Meeting: 16.10.; Second meeting 23.10. (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.



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