| Code |
14529
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| Year |
1
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| Semester |
S1
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| ECTS Credits |
6
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| Workload |
OT(15H)
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| Scientific area |
Informatics
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Entry requirements |
None.
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Learning outcomes |
In this CU we want the students to acquire knowledge relative to a set of topics considered advanced in terms of AI. The students should be able to study, comprehend and explain the contents of scientific papers related to the CU topics.
They should also be able to produce an implementation of one example in one of the studied topics.
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Syllabus |
Some of the topics addressed in this course will be:
-Probabilistic graphical models, such as conditional random fields
-Variational models, mean field models and belief propagation
-Reinforcement learning
-Bayesian approaches to sequential data processing
-Artificial general intelligence
-Robotic applications
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Main Bibliography |
Scientific articles will the chosen according to the topics and the current state-of-the-art.
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Teaching Methodologies and Assessment Criteria |
Assessment is done through two practical assignments (each worth 50% of the final grade). Each assignment includes a written report and a public presentation. The first work is a review of articles in the area and the second has a practical component linked to the implementation of one of the approaches studied.
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Language |
Portuguese. Tutorial support is available in English.
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