Lehrveranstaltung

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

Inhalt

Topic: Deep Learning Life Cycle

Degree Program: M.Sc. AI, M.Sc. WI, M.Sc. ISoSySc, M.Sc. CitH (6 ECTS)
Requirements: Successfully passed the exam to KogSys-ML-M or AI-KI-B (Introduction to AI)
Beneficiaries: Knowledge in programming (Python), practical / hands-on knowledge in deep learning, scientific writing, LaTeX

Description The project provides the opportunity to work in small groups of 3 students in a hands-on fashion. The goal is to understand and implement the different steps to successfully train a deep learning model. We will focus on the advantages and disadvantages of the design choices in data-preprocessing, model training, and model evaluation. You will gain theoretical knowledge about the design choices as well as practical knowledge by implementing these steps. For the implementation, you are expected use Python and the deep learning framework PyTorch. Other libraries are free to choose. At the end of the semester, you will present your results and hand in a technical project report. The project builds on and adds practical experience to the knowledge from corresponding lectures and exercises in the area of machine learning.

Goals Students will familiarize themselves with a specific aspect of robust, explainable machine learning systems. Participants will learn to tackle a research-oriented question or problem independently, with little guidance. This will often involve the critical tasks: literature review, preparation and examination of datasets, implementation and comparison of prototypes, quantitative and qualitative evaluation of approaches. Within small groups, participants will learn to coordinate their project in a team and get comfortable with best practices of software development (e.g., testing, VCS). Documentation and presentation of the project will help to develop both oral (presentation) and written (technical project report) communication skills in a scientific environment. In comparison to the Bachelor Project this Master Project is more ambitious in terms of complexity of selected topics as well as expectations with respect to deliverables and presentations.

Format TBD

Expected workload & Grading
The workload of this module is expected to be roughly as follows:

Attendance of project meetings / presentation: 35h
Literature review and familiarization with topic (individual and within the team): 20h
Implementation of selected algorithm / methodology: 70h
Preparation of presentation: 15h
Written documentation and report: 40h

The grade will be determined in equal parts based on the presentation and report. Attendance of the presentations is mandatory.

Englischsprachige Informationen:

Title:: xAI-Proj-M: Masterproject Explainable Machine Learning
Credits: 6

Zusätzliche Informationen

Erwartete Teilnehmerzahl: 18

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xAI-Proj-M: Masterprojekt Erklärbares Maschinelles Lernen (xAI-Proj-M)

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