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
- 18.02.2022 Introduction & Organisation
- 25.02.2022 Machine Learning Fundamentals I
- 04.03.2022 Machine Learning Fundamentals II
- 11.03.2022 Probability Theory
- 25.03.2022 Linear Feature Regression I
- 01.04.2022 Linear Feature Regression II
- 13.05.2022 Gaussian Processes
- 03.06.2022 Probabilistic Trajectory Models
- 10.06.2022 Exam Preparation and Q&A
- 05.07.2022 Results & Best Practices
- Introduction to Machine Learning (Data and modelling fundamentals)
- Introduction to Probability Theory (Statistics refresher, Bayes Theorem, Common Probability distributions, Gaussian Calculus).
- Linear Probabilistic Regression (Linear models, Maximum Likelihood, Bayes & Logistic Regression).
- Nonlinear Probabilistic Regression (Radial basis function networks, Gaussian Processes, Recent research results in Robotic Movement Primitives, Hierarchical Bayesian & Mixture Models).
- 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.
The course will be graded based on a written exam. 50% of all questions need to be answered correctly to be positive. The exam will take place either online (via Moodle) or in the class room, depending on the current university regulations.
Furthcoming exam dates are:
- 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