190.018 Introduction to Machine Learning (4SH VU, SS)

Elmar Rueckert teaching_currentlectures
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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.

This lecture with integrated exercises 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

Lecture

 

Exercise

 

Assignments

 PointsPresentationSubmissionResults Discussion
Assignment I(3) BP06.0320.0325.03
Assignment II520.0317.0424.04
Assignment III1017.0408.0515.05
Assignment IV1524.0422.0505.06
Assignment V2008.0505.0612.06
 50   

Assignment I is optional and counts only as bonus points.

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 

  • [27.03] Programming exam (50 points)
  • Assignments II to V (50 points)
  • [17.06] Written exam (100 points)
  • up to 16 bonus points

Additional Constraints:

  • Programming exam + assignments II to V min. 50 points to be allowed to take the written exam. Note that with less than 50 points for prog. exam + sum of assignments II to V, you failed the course.
  • Written exam min. 50 points to pass. However, in case you failed you can retake the written exam (in compliance with our university regulations). 

The exams will take place in a computer room and is organized using Moodle. The exam results will be visible on the date of the exam.  

In addition, up to 16 bonus points obtained in regular quiz sessions in the classroom. Note that bonus points can only be obtained when attending the lectures in person. Additionally, 3 bouns points can be obtained through Assignment I. 

Grading scheme: 0-99.9Pts (5), 100-121.9Pts (4), 122-159.9Pts (3), 160-183.9Pts (2), 184-216Pts (1).

Exam dates are:

  • 17.06.2026 at 13:00 – 14:00 CR Hilbert
  • 15.07.2026 at 13:00 – 14:00 CR Hilbert
  • 23.09.2026 at 13:00 – 14:00 TBD
  • 09.12.2026 at 13:00 – 14:00 TBD
  • 03.02.2027 at 13:00 – 14:00 TBD
  • 21.04.2027 at 13:00 – 14:00 TBD

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.