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

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.

190.004 CPS Research Seminar II

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

Presenters are leading invited external speakers, doctoral students, senior researcher, graduates and undergraduates. 

Upcoming Talks

There are no upcoming events.

Location & Time

  • Location: To be decided
  • Dates: To be decided

Past Talks

190.002 Cyber-Physical Systems Lab (2SH P, WS 2022/23)

The exercise will enable the application of modern machine learning techniques and tools in robotics / cyber-physical systems. The following topics will be covered in the course:
   – Kinematics, dynamics & simulation of CPS.
   – Data representations & model learning.
   – Control techniques, priorities & torque control.
   – Planning & cognitive reasoning.
   – Reinforcement learning and black-box optimization.

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 comprehensive understanding of Cyber-Physical-Systems.
  • Students learn to analyze the challenges in simulating, modeling and controlling CPS.
  • Students understand and can apply basic machine learning and control  techniques in CPS.
  • Students know how to analyze the models’ results, improve the model parameters and can interpret the model predictions and their relevance.

Programming Assignments & Simulation Tools

For simulating robotic systems, we will use the tool CoppeliaSim. The tool can be used for free for research and for teaching. 

To experiment with state of the art robot control and learning methods Python will be used. If you never used Python and are unexperienced in programming, please visit the tutorials on Python programming prior to the lecture.  

The course will also use the tool Code With Me from JetBrains. With this stool, we can develop jointly code. 

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.

140.186 Seminar Masterarbeit Montanmaschinenbau (3SH SE, WS21/22, SS 22)

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 Seminar Bachelorarbeit Montanmaschinenbau (8SH SE, WS21/22, SS 22)

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.

150.570/571 Seminar Bachelorarbeit Industrial Data Science (5SH SE, WS21/22, SS 22)

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 22)

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

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

Presenters are leading invited external speakers, doctoral students, senior researcher, graduates and undergraduates. 

Upcoming Talks

There are no upcoming events.

Location & Time

  • Location: To be decided
  • Dates: To be decided

Past Talks

190.001 Cyber-Physical Systems (2SH L, WS 2021/22)

This seminar course provides a unique overview over central topics in Cyber-Physical-Systems:

  1. Kinematics, Dynamics & Simulation of CPS
  2. Data Representations  & Model Learning
  3. Feedback Control, Priorities & Torque Control
  4. Planning & Cognitive Reasoning
  5. Reinforcement Learning & Policy Search

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 comprehensive understanding of Cyber-Physical-Systems.
  • Students learn to analyze the challenges in simulating, modeling and controlling CPS.
  • Students understand and can apply basic machine learning and control  techniques in CPS.
  • Students know how to analyze the models’ results, improve the model parameters and can interpret the model predictions and their relevance.

Programming Assignments & Simulation Tools

For simulating robotic systems, we will use the tool CoppeliaSim. The tool can be used for free for research and for teaching. 

To experiment with state of the art robot control and learning methods Python will be used. If you never used Python and are unexperienced in programming, please visit the tutorials on Python programming prior to the lecture.  

The course will also use the tool Code With Me from JetBrains. With this stool, we can develop jointly code. 

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.

190.013 Exercises in Machine Learning (2SH P, SS 2021/22)

This exercise is part of the lecture 190.012 machine learning

Enrolling for this exercise is a highly recommended but not a requirement for passing the machine learning lecture. 

Course Resources

Python Videolectures and Tutorials

Location & Time

 

Course Topics

The exercise is based on multiple short (typically 2-4 pages) assignments. For most assignments a written report in Latex and Python Code has to be submitted via Email. Each student has to submit an individual assignment report and code.

The topics of the assignments are

  • Creating Latex Documents & Data handling (Latex env. setup, report template, reading & editing data files),
  • Programming in Python & Probability Theory (Python basics, Editor PyCharm, Workflow, variables, functions, classes, plotting, Gaussian distributions, sampling, plotting),
  • Linear Probabilistic Regression (features, least-squares regression derivation & implementation, regularization),
  • Nonlinear Probabilistic Regression (Gaussian Processes, implementation, kernels, predictions)
  • Probabilistic Time-Series-Models (Implementation & Learning from real-world data, Visualization, Predictions).
 
Details to the grading will be presented in the first exercise on the 22.02.2022. For each assignment code templates will be provided.

Learning objectives / qualifications

Hands-on experience with machine learning methods.