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| Detailed information |
| Original study plan |
Master's programme Computer Science 2022W |
| Objectives |
After this class, students will know and understand the basic concepts of a large and centrally important class of methods in modern Artificial Intelligence: Probabilistic Graphical Models, used for representing and reasoning about uncertain information and knowledge in complex real-world scenarios. All three aspects related to such models will be covered: model semantics, inference, and learning. Both basic concepts and specific algorithms will be taught, and all methods will be derived in a mathematically rigorous way. Students will understand how such models can be used for problem modeling and solving, and what their limitations are.
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| Subject |
Elementary Concepts:
- Probability Distributions
- Density Functions
- (conditional) independence
- Probabilistic Reasoning and Inference
Bayesian Networks:
- Representation
- Semantics
- Factorisation
Inference in Bayesian Networks:
Approximate Inference:
- Stochastic Sampling
- Markov Chain Monte Carlo (MCMC) Methods
Learning Bayesian Networks:
- Parameter Learning (maximum likelihood and Bayesian estimation)
- Structure Learning
- Generative vs. Discriminative Models
Special Types of Bayes Nets:
- Linear Gaussian Network Models
Modelling and Predicting Temporal Processes:
- Dynamic Bayes Networks
- Hidden Markov Models
- Kalman Filters
- Particle Filters
- Selected Applications of Probabilistic Graphical Models.
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| Criteria for evaluation |
Written exam at the end of the semester.
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| Methods |
Lecture series with written materials (presentation slides) provided regularly in electronic form.
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| Language |
English |
| Study material |
Koller, Daphne, and Friedman, Nir (2009). Probabilistic Graphical Models. Principles and Techniques. Cambridge, MA: MIT Press.
Russell, Stuart J. and Norvig, P. (2010). Artificial Intelligence: A Modern Approach (3rd Edition). Upper Saddle River, NJ: Prentice Hall.
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| Changing subject? |
No |
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