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
- 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 Filtering, Smoothing and Planning (Classic, Extended & Unscented Kalman Filters, Particle Filters, Gibbs Sampling, Recent research results in Neural Planning).
- 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.
- 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