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.

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

Nothing from December 31, 2020 to January 31, 2021.

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 advanced model learning and reinforcement  techniques to real world problems.
  • 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 CPS we will use the simulator V-REP. For research and for teaching a free eduction version can be found here.

To experiment with state of the art robot control and learning methods Mathworks’ MATLAB will be used. If you do not have it installed yet, please follow the instructions of our IT-Service Center.

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 course is part of the lecture 150.012 machine learning

Enrolling for this exercise is a mandatory requirement for passing the machine learning lecture. 

Links and Resources

Location & Time

Course Topics

The exercise is based on three graded assignments on Probabilistic Regression, Probabilistic Inference and on Probabilistic Optimization. The assignments will include algorithmic implementations in Python and will be presented during the exercise sessions.

The Robot Operating System (ROS) will also be part in some assignments as well as the simulation environment Gazebo. To experiment with state-of-the-art robot control and learning methods Python  will be used. 

Learning objectives / qualifications

Hands-on experience with machine learning methods.

190.012 Machine Learning (2SH L, SS 2021/22)

This course is based on the Probabilistic Machine Learning book by Univ.-Prof. Dr. Elmar Rueckert. 

This book presents fundamental theories, algorithms and concepts of probabilistic
machine learning techniques. It is written for experienced undergraduate or first
semester graduate students.

Links and Resources

Location & Time

Course Topics

  1. Introduction to Probability Theory (Statistics refresher, Bayes Theorem, Common Probability distributions, Gaussian Calculus).
  2. Linear Probabilistic Regression (Linear models, Maximum Likelihood, Bayes & Logistic Regression).
  3. Nonlinear Probabilistic Regression (Radial basis function networks, Gaussian Processes, Recent research results in Robotic Movement Primitives, Hierarchical Bayesian & Mixture Models).
  4. Probabilistic Inference for Filtering, Smoothing and Planning (Classic, Extended & Unscented Kalman Filters, Particle Filters, Gibbs Sampling, Recent research results in Neural Planning).
  5. Probabilistic Optimization (Stochastic black-box Optimizer Covariance Matrix Analysis Evolutionary Strategies & Natural Evolutionary Strategies, Bayesian Optimization).

Learning objectives / qualifications

  • Students get a comprehensive understanding of basic probability theory concepts and methods.
  • Students learn to analyze the challenges in a task and to identify promising machine learning approaches.
  • Students will understand the difference between deterministic and probabilistic algorithms and can define underlying assumptions and requirements.
  • Students understand and can apply advanced regression, inference and optimization techniques to real world problems.
  • Students know how to analyze the models’ results, improve the model parameters and can interpret the model predictions and their relevance.
  • Students understand how the basic concepts are used in current state-of-the-art research in robot movement primitive learning and in neural planning.

Literature

  • The Probabilistic Machine Learning book by Univ.-Prof. Dr. Elmar Rueckert. 
  • Daphne Koller, Nir Friedman. Probabilistic Graphical Models: Principles and Techniques. ISBN 978-0-262-01319-2
  • Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer (2006). ISBN 978-0-387-31073-2.
  • David Barber. Bayesian Reasoning and Machine Learning, Cambridge University Press (2012). ISBN 978-0-521-51814-7.
  • Kevin P. Murphy. Machine Learning: A Probabilistic Perspective. ISBN 978-0-262-01802-9