Subject description
- Bayesian methods: Gaussian processes, Dirichlet processes, MCMC methods, variational inference.
- Deep learning: Boltzmann machines, Autoencoders, Convolutional neural networks.
- Computational learning theory: PAC learning, VC dimension.
- Other select topics: multi-kernel learning, multi-task learning, reinforcement learning.
The subject is taught in programs
Objectives and competences
The main objective is to familliarize the students with advanced machine learning methods. Practical applications and the mathematical and algorithmic background are equally important.
Teaching and learning methods
lectures, lab sessions, homework, consultations
Part of the pedagogical process will be carried out with the help of ICT technologies and the opportunities they offer.
Expected study results
Knowledge and understanding: Understanding advanced machine learning methods and the underlying mathematics and algorithms.
Application: Advanced machine learning methods can be applied to solve the most demanding practical problems in data analysis. The concepts covered by this course are also fundamental to methodological and theoretical research in machine learning.
Reflection: Understanding of the theory on the basis of examples of application. Understanding the relationship between machine leaning and statistics.
Transferable skills: Analytical and research ability. Ability to solve practical data analysis problems.
Basic sources and literature
Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT press.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
Rasmussen, C. E. & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press.
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University of Ljubljana, Faculty of Electrical Engineering Tržaška cesta 25, 1000 Ljubljana