190.013 Introduction to Machine Learning Lab (2SH P, SS)

This course accompanies the 190.012 Introduction to Machine Learning lecture.

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

Format, Location & Time

  • Format: The course format is physical attendance. 
  • Location: HS Kuppelwieser
  • Broadcast: WEBEX for the Q&A sessions
  • Dates: Fridays 13:15 – 14:45 Attention: There is one exception, on the 08.03.24, we start at 12:15!

Learning objectives / qualifications

Hands-on experience with machine learning methods. 

Course Resources

Everybody needs to sign in at MUOnline for the course. Access to Moodle will be essential for all participants in the course, as it will serve as the platform where course materials will be made available. 

Additional Course Resources

Python Videolectures and Tutorials

Contact

Melanie Neubauer via email at melanie.neubauer@unileoben.ac.at



190.012 Introduction to Machine Learning (2SH L, SS)

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

It is written for experienced undergraduates or for first
semester graduate students.

The lecture provides the basic knowledge for the application of modern machine learning methods. It includes an introduction to the basics of data modeling and probability theory. Classical probabilistic linear and non-linear regression methods are derived and discussed using practical examples.

Links and Resources

Location & Time

 

Slides

Course Topics

  1. Introduction to Machine Learning (Data and modelling fundamentals)
  2. Introduction to Probability Theory (Statistics refresher, Bayes Theorem, Common Probability distributions, Gaussian Calculus).
  3. Linear Probabilistic Regression (Linear models, Maximum Likelihood, Bayes & Logistic Regression).
  4. Nonlinear Probabilistic Regression (Radial basis function networks, Gaussian Processes, Recent research results in Robotic Movement Primitives, Hierarchical Bayesian & Mixture Models).
  5. Probabilistic Inference for Time Series (Time series data, basis function models, learning).

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.

Grading

The course will be graded based on a written exam (100 Points). 50% of all questions need to be answered correctly to be positive. The exam will take place in the classroom, or online, depending on the current university regulations.

In addition, up to 10 bonus points obtained in regular quiz sessions in the classroom, and 20% of the achieved points of the Machine Learning Lab will be added to your exam result. Note that bonus points can only be obtained when attending the lectures in person. 

Grading scheme: 0-49.9Pts (5), 50-65.9Pts (4), 66-79Pts (3), 80-91Pts (2), 92-100Pts (1).

Forthcoming exam dates are:

Literature

  • The Probabilistic Machine Learning book by Univ.-Prof. Dr. Elmar Rueckert. 
  • James-A. Goulet. Probabilistic Machine Learning for Civil Engineers. ISBN 978-0-262-53870-1.
  • 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

Note that all books are available at our library or at the chair of CPS. 



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MATLAB Code of Probabilistic Movement Primitives for Motion Analysis

Matlab Code Link

https://cps.unileoben.ac.at/wp/resources/code/MATLAB_ProbabilisticTrajectoryModel_2016Rueckert.zip

Publication where the Code was used

2016

Rueckert, Elmar; Camernik, Jernej; Peters, Jan; Babic, Jan

Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control Journal Article

In: Nature Publishing Group: Scientific Reports, vol. 6, no. 28455, 2016.

Links | BibTeX

Probabilistic Movement Models Show that Postural Control Precedes and Predicts Volitional Motor Control



MATLAB Code of Spiking Neural Networks for Robot Motion Planning

Matlab Code Link

https://cps.unileoben.ac.at/wp/resources/code/MATLAB_SpikingNeuralPlanning_2016Rueckert.zip

Publication where the Code was used

2016

Rueckert, Elmar; Kappel, David; Tanneberg, Daniel; Pecevski, Dejan; Peters, Jan

Recurrent Spiking Networks Solve Planning Tasks Journal Article

In: Nature Publishing Group: Scientific Reports, vol. 6, no. 21142, 2016.

Links | BibTeX

Recurrent Spiking Networks Solve Planning Tasks



Stochastic Neural Networks for Robot Motion Planning

Video

Link to the file

You may use this video for research and teaching purposes. Please cite the Chair of Cyber-Physical-Systems or the corresponding research paper. 

Publications

2016

Tanneberg, Daniel; Paraschos, Alexandros; Peters, Jan; Rueckert, Elmar

Deep Spiking Networks for Model-based Planning in Humanoids Proceedings Article

In: Proceedings of the International Conference on Humanoid Robots (HUMANOIDS), 2016.

Links | BibTeX

Deep Spiking Networks for Model-based Planning in Humanoids

Rueckert, Elmar; Kappel, David; Tanneberg, Daniel; Pecevski, Dejan; Peters, Jan

Recurrent Spiking Networks Solve Planning Tasks Journal Article

In: Nature Publishing Group: Scientific Reports, vol. 6, no. 21142, 2016.

Links | BibTeX

Recurrent Spiking Networks Solve Planning Tasks



Learning Bimanual Manipulation Primitives

Video

Link to the file

You may use this video for research and teaching purposes. Please cite the Chair of Cyber-Physical-Systems or the corresponding research paper. 



Learning Multimodal Solutions with Movement Primitives

Video

Link to the file

You may use this video for research and teaching purposes. Please cite the Chair of Cyber-Physical-Systems or the corresponding research paper. 

Publications

2015

Rueckert, Elmar; Mundo, Jan; Paraschos, Alexandros; Peters, Jan; Neumann, Gerhard

Extracting Low-Dimensional Control Variables for Movement Primitives Proceedings Article

In: Proceedings of the International Conference on Robotics and Automation (ICRA), 2015.

Links | BibTeX

Extracting Low-Dimensional Control Variables for Movement Primitives



Ich zeig dir, wie’s geht!

Print Media Article 54° Nord (Nr. 4/2018 page 57)

Links

Link to the PDF.



Dynamic Control of a CableBot

Building a CableBot and Learning the Dynamics Model and the Controller

Controlling cable driven master slave robots is a challenging task. Fast and precise motion planning requires stabilizing struts which are disruptive elements in robot-assisted surgeries. In this work, we study parallel kinematics with an active deceleration mechanism that does not require any hindering struts for stabilization. 

Reinforcement learning is used to learn control gains and model parameters which allow for fast and precise robot motions without overshooting. The developed mechanical design as well as the controller optimization framework through learning can improve the motion and tracking performance of many widely used cable-driven master slave robots in surgical robotics.

Project Consortium

  • Montanuniversität Leoben

Related Work

H Yuan, E Courteille, D Deblaise (2015). Static and dynamic stiffness analyses of cable-driven parallel robots with non-negligible cable mass and elasticity, Mechanism and Machine Theory, 2015 – Elsevier, link.

MA Khosravi, HD Taghirad (2011). Dynamic analysis and control of cable driven robots with elastic cables, Transactions of the Canadian Society for Mechanical Engineering 35.4 (2011): 543-557, link.

Publications

2019

Rueckert, Elmar; Jauer, Philipp; Derksen, Alexander; Schweikard, Achim

Dynamic Control Strategies for Cable-Driven Master Slave Robots Proceedings Article

In: Keck, Tobias (Ed.): Proceedings on Minimally Invasive Surgery, Luebeck, Germany, 2019, (January 24-25, 2019).

Links | BibTeX

Dynamic Control Strategies for Cable-Driven Master Slave Robots