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Artificial Intelligence

Code 11564
Year 3
Semester S1
ECTS Credits 6
Workload PL(30H)/T(30H)
Scientific area Informatics
Entry requirements None.
Mode of delivery Face-to-face
Learning outcomes This course unit presents algorithms and computational techniques currently used in Artificial Intelligence and encourages their application to problem solving.
At the end of this course students should be able to:
- Characterize Artificial Intelligence, its approaches, fundamentals and applications
- Develop computational agents using state-space search techniques, learning, adaptation and knowledge representation.
Syllabus S1. Introduction.
S2. Problem solving.
S3. Knowledge representation.
S4. Uncertain knowledge and reasoning.
S5. Machine learning.
S6. Applications: perception, robotics e NLP.
Main Bibliography Stuart Russell and Peter Norvig, Artificial Intelligence: A modern approach, 3rd edition, Prentice Hall, 2010.
PDFs of the theoretical classes.

Teaching Methodologies and Assessment Criteria This CU has both theoretical and prctical classes.
In the theoretical classes the teacher presents the course materials and promotes its discussion with the students. In the practical classes, the students solve practical problems using the Python programming language.

Theoretical tests (14 points - 70%) - 2 tests
Practical Project (6 points - 30%)
Presence in the 80% of the classes is required.
Language Portuguese. Tutorial support is available in English.
Last updated on: 2020-01-20

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