Leon Maximilian Helmich

M.Sc.
Research Assistant

Contact

Leon Maximilian Helmich
E-6 Elektrische Energietechnik
  • Elektrische Energietechnik
Harburger Schloßstraße 22a,
21079 Hamburg
Building HS22a, Room 2.005
Phone: +49 40 42878 2379
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Research Project

iTherNet
Intelligent Thermal Networks - New cooling technologies and energy-optimized operating concepts

iTherNet

Intelligent Thermal Networks - New cooling technologies and energy-optimized operating concepts

 Federal Ministry for Economic Affairs and Climate Action (BMWK); Duration: 2020 - 2023

Publications

TUHH Open Research (TORE)

2023

2021

Courses

Stud.IP
zur Veranstaltung in Stud.IP Studip_icon
Seminare.EIM: Deep Reinforcement Learning (DSBS, CSMS, IIWMS, TMBS, IMPICS)
Semester:
SoSe 24
Veranstaltungstyp:
Seminar (Lehre)
DozentIn:
Dr. rer. nat. Pradeep Banerjee
Beschreibung:
This course is a basic introduction to Deep Reinforcement Learning (RL). In RL, an agent learns to make sequential decisions by interacting with an environment to maximize some notion of reward. Deep RL combines RL and deep learning, in that neural networks are used to represent the agent's value functions or decision making policies, enabling the handling of complex input spaces such as images or sensor readings. This approach has led to significant advancements in tackling problems such as playing video games, robotics control, and autonomous driving. As a result, expertise in RL constitutes a significant advantage in the industrial job market. By the end of the seminar, it is expected that students will gain proficiency in designing their own RL algorithms, enabling them to apply it to different areas such as robotics, recommendation systems, gaming, etc. to name a few, and also comprehend current literature in the field.
TeilnehmerInnen:
The seminar is aimed at all Bachelor- and Master- level students in the Informatik and the Techno-Mathematik courses. A maximum of 12 students can participate in the seminar.
Voraussetzungen:
As a prerequisite, this seminar will assume familiarity with probability, linear algebra, and programming in Python.
Lernorganisation:
The seminar is divided into six blocks (following an introductory session), each lasting two weeks. Every block consists of the following components: * Week 1: Preparation of a presentation using prescribed sources (book chapters, video lectures, scientific articles). * Week 2: Presentations by 2 participants, each lasting 25 minutes based on a topic assigned to each participant in the first session of the seminar.
Bereichseinordnung:
Studiendekanat Elektrotechnik, Informatik und Mathematik
Weitere Informationen aus Stud.IP zu dieser Veranstaltung
Heimatinstitut: Studiendekanat Elektrotechnik, Informatik und Mathematik (E)
In Stud.IP angemeldete Teilnehmer: 10
Anzahl der Dokumente im Stud.IP-Downloadbereich: 3

Supervised Theses

ongoing

2024

  • Eberhard, F. (2024). Estimating AC and DC losses in the inductor of a switch mode power supply..

completed