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150.033 Do-it Lab IDS 3 (1SH P, SS )

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You have no prior experience with deep learning or robots but would like to work with them?

If so, this hands-on project will enable you to build and control your state-of-the-art robotic devices, such as compliant robot arms, five-fingered robot hands, mobile robots, legged robots, or tactile and visual sensors.

You will use Python for programming. Prior experience is beneficial but not mandatory. 

At the end of the practical project, we discuss your achievements and what you have learnt.

You can work on your own or build a team of up to three people at most. We provide a student lab with high-performance pcs with RTX 4090 graphics cards and student rooms.

The project is based on code examples, wiki pages and video tutorials for non-experts.

Links and Resources

Location & Time

Learning objectives / qualifications

  • Students get a practical experience in working, programming and understanding autonomous robots in navigation and obstacle avoidance tasks.
  • Students understand and can apply classical robot path planning and navigation algorithms.
  • Students learn how to present their implementation, assumptions and achievements.


190.006 Seminar for Doctoral Students (4SH, WS)

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Univ.-Prof. Dr. Elmar Rueckert is organizing this doctoral seminar.

The goals of this course are

  • Instruction in the scientific treatment of problems in machine learning,
    robotics and cyber-physical systems.
  • Presentation and defense of own hypotheses in the field of the respective dissertation.
  • Guidelines for writing scientific papers at an international level.
  • Discussion of the content and structure of a doctoral thesis.
  • Discussion of CVs with examples of Ph.Ds., Postdocs, early career stage professors and of full professors.
  • Discussion on potential career paths and differences in the individual systems.

Language:
English only

You are a doctoral student and would like to learn how AI achievements are presented, defended, and discussed?

This course will give you the opportunity to discuss all aspects of a doctoral thesis and of potential career paths in AI. Univ.-Prof. Dr. Elmar Rueckert will discuss best practices in publishing, presenting and how to get the ideal future job.

Dates

    • 07.10.22 13:15 Univ.-Prof. Dr. Elmar Rueckert gives an Introduction to the content of the doctoral seminar. Also online via: https://unileoben.webex.com/meet/elmar.rueckert
    • 14.10.22 13:15 – 17:30
    • 21.10.22 13:15 – 17:30
    • 28.10.22 13:15 – 17:30
    • 04.11.22 13:15 – 17:30
    • 11.11.22 13:15 – 17:30
    • 18.11.22 13:15 – 17:30
    • 25.11.22 13:15 – 17:30
    • 02.12.22 13:15 – 17:30
    • 09.12.22 13:15 – 17:30
    • 16.12.22 13:15 – 17:30
    • 13.01.23 13:15 – 17:30
    • 20.01.23 13:15 – 17:30
    • 27.01.23 13:15 – 17:30

Location & Time

Links and Resources



560.002 Do-it Lab Mechanical Engineering (1SH P, WS )

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You have no prior experience with robots but would like to work with them?

If so, this hands-on project will enable you to build and control your own robot.

You will use Python to program intelligent navigation or even learning strategies. 

At the end of the practical project, we discuss your achievements and what you have learnt.

You can work on your own or build a team of up to three people at most. We provide a student lab with all-in-one-pcs prepared to code in Python on an ubuntu os.

The project is based on code examples, wiki pages and video tutorials for non-experts.

Links and Resources

Location & Time

Learning objectives / qualifications

  • Students get a practical experience in working, programming and understanding autonomous robots in navigation and obstacle avoidance tasks.
  • Students understand and can apply classical robot path planning and navigation algorithms.
  • Students learn how to present their implementation, assumptions and achievements.


190.014 Integrated CPS Projekt (3SH SE, SS & WS)

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You are interested in working with real robots and want to understand how such machines ‘learn’?

This project will enable you to dig into the fascinating world of robot learning.

You will work alone or in a team on modern, state-of-the art hardware at the Chair of CPS.

We offer complex robotic systems, powerful PCs and GPU clusters to work with.

The course provides a structured and well motivated overview over modern techniques and tools which enable the students to define learning problems in Cyber-Physical-Systems. 

Selected Topics (Many more are available upon request)

Tasks

  • Select a topic from the presented topics at the CPS chair 
  • Build a team of 3-4 students.
  • Send an E-Mail with all details (topic, members) to the supervisor (E-Mails are on the CPS webpage).
  • Keep track of your tasks and working hours (spreadsheet with tasks, hours per day, and members).
  • Send a 1-page concept of your project (tasks, outcomes, milestones, est. work hours, meetings with the supervisor) to us.
  • Create a git repository on our studgit.cps.unileoben.ac.at
  • Implement your project.
  • Write a readme AND a wiki in the git repository. Dokument how you used AI tools like chatGPT. Translation and spelling tools can be neglected. 
  • Create slides to present your work at 24.06.2024 12:00-15:00 HS Thermoprozesstechnik

Grading

  • 80 points for the project results (code, effort, team management, git readme, and git wiki).
  • 20 points for the final presentation (20 min per group + 5 min. questions).
  • up to 20 bonus points for extraordinary efforts. 

 

Cumulative Points

Final Grade

0 – 49.9

5

50 – 65.9

4

66 – 79.9

3

80 – 91.9

2

92 – 100

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Links and Resources

Location & Time

  • Location: Independent project team work, or for real robot projects in the Laboratories at the Chair of Cyber-Physical-Systems.
  • Dates:
    • Regular, typically weekly meetings, with your supervisor(s). 
    • Final presentation on the 26.06.2024 at 12:00 in HS TPT.

Learning objectives / qualifications

  • Students get a practical experience in working, modeling and simulating Cyber-Physical-Systems.
  • Students understand and can apply advanced model learning and reinforcement  techniques to real world problems.
  • Students learn how to write scientific reports.

Literature

  • The Probabilistic Machine Learning book by Univ.-Prof. Dr. Elmar Rueckert. 
  • Bishop 2006. Pattern Recognition and Machine Learning, Springer. 
  • Barber 2007. Bayesian Reasoning and Machine Learning, Cambridge University Press. 
  • Murray, Li and Sastry 1994. A mathematical introduction to robotic manipulation, CRC Press. 
  • B. Siciliano, L. Sciavicco 2009. Robotics: Modelling,Planning and Control, Springer.
  • Kevin M. Lynch and Frank C. Park 2017. MODERN ROBOTICS, MECHANICS, PLANNING, AND CONTROL, Cambridge University Press.


190.004 CPS Research Seminar II (2SH SE, WS)

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Univ.-Prof. Dr. Elmar Rueckert is organizing this research seminar. Topics include research in AI, machine and deep learning, robotics, cyber-physical-systems and process informatics. 

Language:
English only

Are you an undergraduate,  graduate, or doctoral student and want to learn more about AI?

This course will give you the opportunity to listen to research presentations of latest achievements. The target audience are non-experts. Thus no prior knowledge in AI is required.

To get the ECTS credits, you will select a research paper, read it and present it within the research seminar (10-15 min presentation). Instead of selecting a paper of our list, you can also suggest a paper. This suggestion has to be discussed with Univ.-Prof. Dr. Elmar Rueckert first.

After the presentation, the paper is discussed for 10-15 min.

Further, external presenters that  are leading researchers in AI will be invited. External speakers will present their research in 30-45 min, followed by a 15 min discussion. 

Links and Resources

Location & Time

  • Location: HS Thermoprozesstechnik (HS TPT) with some exceptions, see the list of dates below.
  • Dates: On selected Wednesdays 12:15-14:00. Talks will be announced via MUOnline. Note: Therefore, it is important to register for the course.  

List of Talks and Dates

  • 25.10.2023 12:15 (HS TPT)
    • Tutorial: Björn Ellensohn, Docker and other Cloud Services for research and teaching.
  • 22.11.2023 12:15 (HS TPT)
    • Research Talk: Dr. Ozan Özdenizci (TU Graz), on robust and secure deep learning.

Available Research Papers to Select

 



140.186 & 560.150 Seminar Master Work – Mechanical Engineering (3SH SE, WS & SS)

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You are interested in working with modern robots or want to understand how such machines ‘learn’?

If so, this bachelor seminar will enable you to dig into the fascinating world of robot learning. You will implement and apply modern machine learning algorithms in Python, Matlab or C++/ROS. 

Your learning or control algorithm will be evaluated in cyber-physical-systems. Find out which theses are currently supervised and offered. 

 

Links and Resources

Location & Time

Learning objectives / qualifications

  • Students will work on controlling, modeling and simulating Cyber-Physical-Systems and autonomously learning systems.
  • Students understand and can apply advanced model learning and reinforcement  techniques to real world problems.
  • Students learn how to write scientific reports.

Literature

  • The Probabilistic Machine Learning book by Univ.-Prof. Dr. Elmar Rueckert. 
  • Bishop 2006. Pattern Recognition and Machine Learning, Springer. 
  • Barber 2007. Bayesian Reasoning and Machine Learning, Cambridge University Press. 
  • Murray, Li and Sastry 1994. A mathematical introduction to robotic manipulation, CRC Press. 
  • B. Siciliano, L. Sciavicco 2009. Robotics: Modelling,Planning and Control, Springer.
  • Kevin M. Lynch and Frank C. Park 2017. MODERN ROBOTICS, MECHANICS, PLANNING, AND CONTROL, Cambridge University Press.


140.185 & 560.100 Seminar Bachelor Work – Mechanical Engineering (8SH SE, WS & SS)

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You are interested in working with modern robots or want to understand how such machines ‘learn’?

If so, this bachelor seminar will enable you to dig into the fascinating world of robot learning. You will implement and apply modern machine learning algorithms in Python, Matlab or C++/ROS. 

Your learning or control algorithm will be evaluated in cyber-physical-systems. Find out which theses are currently supervised and offered. 

 

Links and Resources

Location & Time

Learning objectives / qualifications

  • Students will work on controlling, modeling and simulating Cyber-Physical-Systems and autonomously learning systems.
  • Students understand and can apply advanced model learning and reinforcement  techniques to real world problems.
  • Students learn how to write scientific reports.

Literature

  • The Probabilistic Machine Learning book by Univ.-Prof. Dr. Elmar Rueckert. 
  • Bishop 2006. Pattern Recognition and Machine Learning, Springer. 
  • Barber 2007. Bayesian Reasoning and Machine Learning, Cambridge University Press. 
  • Murray, Li and Sastry 1994. A mathematical introduction to robotic manipulation, CRC Press. 
  • B. Siciliano, L. Sciavicco 2009. Robotics: Modelling,Planning and Control, Springer.
  • Kevin M. Lynch and Frank C. Park 2017. MODERN ROBOTICS, MECHANICS, PLANNING, AND CONTROL, Cambridge University Press.


150.570 Seminar Bachelor Work – Industrial Data Science (5SH SE, WS & SS)

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You are interested in working with modern robots or want to understand how such machines ‘learn’?

If so, this bachelor thesis will enable you to dig into the fascinating world of robot learning. You will implement and apply modern machine learning algorithms in Python, Matlab or C++/ROS. 

Your learning or control algorithm will be evaluated in cyber-physical-systems. Find out which theses are currently supervised and offered. 

 

Links and Resources

Location & Time

Learning objectives / qualifications

  • Students will work on controlling, modeling and simulating Cyber-Physical-Systems and autonomously learning systems.
  • Students understand and can apply advanced model learning and reinforcement  techniques to real world problems.
  • Students learn how to write scientific reports.

Literature

  • The Probabilistic Machine Learning book by Univ.-Prof. Dr. Elmar Rueckert. 
  • Bishop 2006. Pattern Recognition and Machine Learning, Springer. 
  • Barber 2007. Bayesian Reasoning and Machine Learning, Cambridge University Press. 
  • Murray, Li and Sastry 1994. A mathematical introduction to robotic manipulation, CRC Press. 
  • B. Siciliano, L. Sciavicco 2009. Robotics: Modelling,Planning and Control, Springer.
  • Kevin M. Lynch and Frank C. Park 2017. MODERN ROBOTICS, MECHANICS, PLANNING, AND CONTROL, Cambridge University Press.


150.510 Industrial Data Science Projekt (8SH SE, SS)

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You are interested in working with modern robots or want to understand how such machines ‘learn’?

If so, this project will enable you to dig into the fascinating world of robot learning.

The course provides a structured and well motivated overview over modern techniques and tools which enable the students to define learning problems in Cyber-Physical-Systems. 

Links and Resources

Location & Time

Learning objectives / qualifications

  • Students get a practical experience in working, modeling and simulating Cyber-Physical-Systems.
  • Students understand and can apply advanced model learning and reinforcement  techniques to real world problems.
  • Students learn how to write scientific reports.

Literature

  • The Probabilistic Machine Learning book by Univ.-Prof. Dr. Elmar Rueckert. 
  • Bishop 2006. Pattern Recognition and Machine Learning, Springer. 
  • Barber 2007. Bayesian Reasoning and Machine Learning, Cambridge University Press. 
  • Murray, Li and Sastry 1994. A mathematical introduction to robotic manipulation, CRC Press. 
  • B. Siciliano, L. Sciavicco 2009. Robotics: Modelling,Planning and Control, Springer.
  • Kevin M. Lynch and Frank C. Park 2017. MODERN ROBOTICS, MECHANICS, PLANNING, AND CONTROL, Cambridge University Press.


190.003 CPS Research Seminar I (2SH SE, SS)

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Univ.-Prof. Dr. Elmar Rueckert is organizing this research seminar. Topics include research in AI, machine and deep learning, robotics, cyber-physical-systems and process informatics. 

Language:
English only

Are you an undergraduate,  graduate, or doctoral student and want to learn more about AI?

This course will give you the opportunity to listen to research presentations of latest achievements. The target audience are non-experts. Thus no prior knowledge in AI is required.

To get the ECTS credits, you will select a research paper, read it and present it within the research seminar (10-15 min presentation). Instead of selecting a paper of our list, you can also suggest a paper. This suggestion has to be discussed with Univ.-Prof. Dr. Elmar Rueckert first.

After the presentation, the paper is discussed for 10-15 min.

Further, external presenters that  are leading researchers in AI will be invited. External speakers will present their research in 30-45 min, followed by a 15 min discussion.

Location & Time

List of Talks and Dates

The specified time windows do not include discussions. 

Some Research Paper Candidates