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Institute of Electrical Power and Energy Technology
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IEET > Staff > Honorary Professor > Prof. Dr.-Ing. Roland Harig

Prof. Dr.-Ing. Roland Harig

Honorary Professor

Contact

Prof. Dr.-Ing. Roland Harig
E-6 Elektrische Energietechnik
  • Elektrische Energietechnik
Office Hours
nach Vereinbarung
Harburger Schloßstraße 36,
21079 Hamburg
Building HS36, Room C2 1.009

Previous activity

until 03/2015
Head of Research Area Optical Measurement Technology (Infrared Measurment Technology) at the Institute of Measurement Technology / TUHH

Publications

TUHH Open Research (TORE)

2012

2011

2008

Courses

Stud.IP
link to course in Stud.IP Studip_icon
Machine Learning for Physical Systems (VL)
Subtitle:
This course is part of the module: Machine Learning for Physical Systems
Semester:
WiSe 23/24
Course type:
Lecture
Course number:
lv2987_w23
Lecturer:
Prof. Dr. Roland Aydin, Maire Henke
Description:
Introduction into various approaches and methods for using Machine Learning in conjunction with physical systems. \n Topics include: - Data pre- and postprocessing, classification versus regression - Decision-trees and random forests - Convolutional Neural Networks (CNNs) - Feature selection - Neural architecture search (NAS) and hyperparameter tuning - Constitutive artificial neural networks (CANNs) - Synthetic data - Multimodal and ensemble learning - Optimal experimental design (active learning) - Large Language Models - Process-structure-properties machine learning pipelines All these methods are useful in non-physical domains as well, the focus of the lecture and exercise will be their usability for physical systems. The associated exercise sessions (on the same day) will make use of various Python-libraries such as Sklearn and Pytorch, usually using Jupyter notebooks. Knowledge from the exercises will be relevant for the lecture and vice versa. No prior knowledge in machine learning or Python programming is strictly required, although it would be beneficial.
Performance accreditation:
m1807-2022 - Machine Learning for Physical Systems<ul><li>p1888-2023 - Machine Learning for Physical Systems: Klausur schriftlich</li></ul>
ECTS credit points:
3
Stud.IP informationen about this course:
Home institute: Machine Learning in Virtual Materials Design (M-EXK5)
Registered participants in Stud.IP: 86
Postings: 3
Documents: 15
TU Hamburg
Institute of Electrical Power and Energy Technology (ieet) E-6
Prof. Dr.-Ing. Christian Becker
Harburger Schloßstraße 36
21079 Hamburg
E-mail : ieet(at)tuhh.de
Phone : +49 40 42878 3213
Fax : +49 40 42878 2382
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