Probabilistic Graphical Models
Instance: 2020/2021 - 2S (of 08-02-2021 to 30-07-2021)
Cycles of Study/Courses
Teaching Staff - Responsibilities
Teaching - Hours
|Theoretical and practical :
This unit aims to empower the students with necessary knowledge and skills to use modern methods of probabilistic reasoning for biomedical problems, more specifically regarding theory and practice of Bayesian networks for interdependencies exploration and clinical decision support.
Learning outcomes and competences
- Identify different types of uncertainty inherent in clinical practice.
- Understand the main objectives of Bayesian inference for clinical decision support.
- Describe the theory of Bayesian networks, its objectives and the main features.
- Observe clinical applications of Bayesian networks in different areas and outcomes.
- Motivation and examples
- Probability and medical applciations
- Probabilistic graphical models
- Introduction to Bayesian networks
- Bayesian networks
- Semantics and factorization
- Probabilistic influence flow
- Conditional independence andnaive Bayes
- Causal independence
- Temporal Bayesian networks
- Building Bayesian networks from data
- Machine learning
- Bayesian network parameter estimation
- Bayesian network structure learning
- Learning and inferring from incomplete data
Darwiche, A. ; Modeling and Reasoning with Bayesian Networks, Cambridge University Press, 2009
Darwiche, A. ; Bayesian networks, Communications of the ACM, 53(12), 80–90, 2010
Lucas, P. J. F., van der Gaag, L. C., & Abu-Hanna, A. ; Bayesian networks in biomedicine and health-care, Artificial Intelligence in Medicine, 30(3), 201–14., 2004
Lucas, P. ; Bayesian analysis, pattern analysis, and data mining in health care, Current Opinion in Critical Care, 10(5), 399–403., 2004
Koller, D., & Friedman, N. ; Probabilistic Graphical Models - Principles and Techniques, MIT Press., 2009
Cowell, R. G., Dawid, P., Lauritzen, S. L., & Spiegelhalter, D. J. ; Probabilistic Networks and Expert Systems: Exact Computational Methods for Bayesian Networks, Springer, 2007
Teaching methods and learning activities
Theoretical lectures and practical lessons, with topic discussion, individual and group exercises, and hands-on training with proper software
Distributed evaluation with final exam
Amount of time allocated to each course unit
|Apresentação/discussão de um trabalho científico
|Frequência das aulas
Eligibility for exams
Delivery of an assignment during the evaluation process.
Calculation formula of final grade
Evaluation will be based on practical assignments (80%) and final exam (20%).