| 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 |
The CU works in a tutorial way, so there will be brief presentations of the topics studied to the students, scientific papers will be chosen in the areas of study and the students will studied and present them and an implementation of a chosen method and its presentation will be also done.The students will be assessed using the 2 works they will do throughout the semester: a first one with a set of articles that will have to be read and presented and a second one related to the computational implementation of one of the methods studied. In both cases the presentations made and the reports produced will be evaluated. Each of the works will be worth 10 values out of 20.
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Language |
Portuguese. Tutorial support is available in English.
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