Informatik

Beschreiben Sie kurz und prägnant, worum es in diesem Kurs geht.

Graph Drawing: Algorithms for the Visualization of Graphs

This course covers the most important algorithms to draw graphs. Methods from the course Algorithmische Graphentheorie (Algorithmic Graph Theory) such as divide and conquer, flow networks and integer programming will be used. We will become familiar with measures of quality of a graph drawing as well as algorithms that optimize these measures.

Our goal is to get an overview of graph visualization and familiarize with common tools in order to consolidate our knowledge about the modeling and solving of problems with the help of graphs and graph algorithms.

The Ethical Hacking Lab is a practical course about network security, popular attacks, and defenses, as well as hands-on experience exploiting and defending against vulnerabilities. The lab is interleaved with Cisco Netacad, so it is possible to get a Cisco Netacad Cybersecurity Operations certificate through a separate exam.

The practicum is divided into two parts. First, the theoretical part is provided all semester long and offers an overview of Ethical Hacking and its technical basics. This segment needs to be completed online before the practical part starts and is examined with a grade. The second part is a compact course in the semester break in August or September. This segment focuses on hands-on offensive security and dives into secure programming, rootkits, and botnets.

Master students are welcome to participate in this course for their master practicum (10-I-EHL1). The practical assignments can be completed on the computers available in the Cisco lab. 

Please contact Christoph Sendner (christoph.sendner@uni-wuerzburg.de) via mail with your matriculation number and chosen module (i.e., EHL1) for registration. 

No more registrations!

- Von Reglern, Beobachtern, Schätzern und Filtern - Anhand dieser Kette von Themen ist das Ziel der Vorlesung die Herleitung und Anwendung des Kalman-Filters. Die Vorlesung wird durch Simulationsexperimente und Hardware-Experimente begleitet, um die erlernten Methoden anzuwenden und deren Möglichkeiten kennenzulernen.

Beschreiben Sie kurz und prägnant, worum es in diesem Kurs geht.

In this Master's practical course, the students will deal with Deep Learning (DL) methods for spatio-temporal data analysis. The students will individually work with a meteorological dataset and develop different approaches for solving a given problem. To do this, students draw upon current approaches from the scientific literature. At the end of the semester, the different approaches are presented to the entire group and an evaluation on a common held-out test dataset is performed.

The focus of this course is on the theoretical development, practical implementation and scientifically sound evaluation of new Deep Learning approaches for spatio-temporal data.

Beschreiben Sie kurz und prägnant, worum es in diesem Kurs geht.

Beschreiben Sie kurz und prägnant, worum es in diesem Kurs geht.

Beschreiben Sie kurz und prägnant, worum es in diesem Kurs geht.

Beschreiben Sie kurz und prägnant, worum es in diesem Kurs geht.

Beschreiben Sie kurz und prägnant, worum es in diesem Kurs geht.

Beschreiben Sie kurz und prägnant, worum es in diesem Kurs geht.

Informationen zu den Themen, Ablauf und Bewerbung für ein Seminar auf der Homepage des Lehrstuhls: https://info3.org/

Information on the topics, procedure and application for a seminar on the chair's homepage: https://info3.org/

Das methodische Wissen und die praktischen Fähigkeiten zur Leistungsbewertung und Modellierung verteilter Systeme am Beispiel des Internet of Things (IoT) werden vermittelt. Dies umfasst Wahrscheinlichkeitstheorie und Warteschlangentheorie, insbesondere Markov Modelle. Die Markov Modelle spielen eine zentrale Rolle in vielen Bereichen der Informatik, etwa bei Entscheidungsproblemen (z.B. Navigation von Robotern) oder Machine Learning Verfahren (z.B. Spracherkennung). In diesem Kurs fokussieren wir auf den Anwendungsbereich IoT der Kommunikationsnetze.

Dear Participants,

The seminar will start on May 23rd at 14:00 and will take place in the Seminar room 1 of the new CAIDAS building in Emil-Fischer-Str. 50.

This seminar on Reinforcement Learning (RL) will cover both classical approaches and modern techniques based on deep learning methods. The seminar will consist of an introductory lecture given by Prof. Carlo D'Eramo, where several topics will be described. Following this, participants will have to select a topic of their choice, and study related scientific papers, journals, etc. The examination will consist of an oral presentation and a discussion (30min + 10min), and the writing of a report on the studied material (6-8 pages). The deadline for delivering the report will be 2 weeks after the oral presentation.

Best,

Carlo D'Eramo

This course aims at providing an introduction to reinforcement learning (RL) by covering the main topics ranging from classic approaches to modern techniques, with a particular focus on the groundbreaking integration of deep learning techniques in reinforcement learning algorithms.

The course is organized into 12 lectures with the following topics:

Lecture 01: Introduction to Reinforcement Learning and Decision-Making;
Lecture 02: Markov Decision Processes (MDPs);
Lecture 03: Dynamic Programming;
Lecture 04: Multi-Armed Bandits and Exploration vs Exploitation;
Lecture 05: Model-Free Evaluation;
Lecture 06: Model-Free Control;
Lecture 07: Reinforcement Learning with value function approximation;
Lecture 08: Introduction to Deep Reinforcement Learning;
Lecture 09: Policy Gradient;
Lecture 10: Introduction to Actor-Critic Approaches;
Lecture 11: Deep Actor-Critic Algorithms;
Lecture 12: Model-Based RL and Monte-Carlo Tree Search.

At the end of the course, the students will have developed the required theoretical and empirical knowledge about classic and modern reinforcement learning (RL) approaches. They will have a good knowledge of the core problems and goals of RL, its current state-of-the-art, and its future challenges. From a practical point of view, they will be able to design, implement, and carry out RL experiments using common tools, e.g., python libraries for RL and optimization. They will be able to conduct their M.Sc. theses on topics related to RL, and they will have good starting knowledge of RL for a future career in research or industry.

The evaluation consists of a written exam and 2 coding assignments.
The exam is in English and lasts 2 hours. There will be an exam at the end of the Summer Semester and one at the beginning of the Winter Semester. It will consist of open questions and/or quick application of algorithms by hand;
The coding assignments will be based on the technical skills developed in the exercise session, and will consist of autonomous implementation of algorithms, execution of experiments, and presentation of obtained results in a report of 2 pages. The two coding assignments will give, respectively, an additional bonus of 0.3 and 0.4 to the final grade of the written exam.

The course is organized into 12 lectures and 13 exercise sessions. The lectures will provide both theoretical and practical perspectives on the respective topics. The exercise session will consist of a quick refresh of the topic of the lecture and a guided coding session using the Python programming language to implement and run reinforcement learning experiments.

The main reference book for the course is:
- Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. MIT press, 2018. (http://incompleteideas.net/book/the-book.html).
Additional references are:
- Szepesvári, Csaba. "Algorithms for reinforcement learning." Synthesis lectures on artificial intelligence and machine learning 4.1 (2010): 1-103. (https://sites.ualberta.ca/~szepesva/papers/RLAlgsInMDPs.pdf);
- Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong. Mathematics for machine learning. Cambridge University Press, 2020.