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Artificial Intelligence 2016/2017

  • 5 ECTS
  • Taught in Portuguese
  • Continuous Assessment

Objectives

Students shall understand how intelligent systems are built, integrating solutions for real problems.
They shall understand the role of problem solving and learning techniques on the construction of intelligent systems.
Students shall be able to:
1. Formulate search problems in state spaces; understand the pluses and minuses of each search method, being able to select the best one for each problem.
2. Write programs to solve search problems in state spaces.
3. Identify, characterize and solve constraint satisfaction problems.
4. Characterize and solve simple multi-agent problems.
5. Extract knowledge from examples.
6. Understand neural network learning.
7. Apply statistical reasoning to learning and classification problems.

Recommended Prerequisites

Java programming

Teaching Metodology

During theoretical-practical classes the subjects will be explained with practical examples and discussion.
During practical classes students will work on several assignments, having the opportunity to program the studied algorithms.

Body of Work

1. What is AI
2. Intelligent agents. Types of agents. Environments.
3. Problem solving by search. Problem formulation. Search strategies.
4. Non-informed and informed search.
5. Constraint satisfaction.
6. Games. Mini-max algorithm. Alfa-beta pruning.
7. Learning. Decision trees. Nearest neighbour classifier.
8. Neural networks.
9. Theorem of Bayes. Naive-Bayes classifier.

Recommended Bibliography

Stuart J. Russel and Peter Norvig. Artificial Intelligence - A Modern Approach, Pearson, Third edition, 2010, ISBN: 978-0-13-207148-2.

Ernesto Costa e Anabela Simões. Inteligência Artificial - Fundamentos e Aplicações, FCA, Segunda Edição, 2008, ISBN 978-972-722-340-4.

Weekly Planning

1. Introduction. History of AI. Agentes. Environments.
2. Types of agents. Search.
3. Non-informed search.
4. Informed search.
5. Problem resolution.
6. Constraint satisfaction.
7. Games. Mini-max algorithm. Alfa-beta pruning.
8. Learning. Nearest neighbour classifier
9. Test (date to be confirmed).
10. Decision trees.
11. Problem resolution.
12. Theorem of Bayes.
13. Naive-Bayes classifier.
14. Neural networks.
15. Test (date to be confirmed).

Demonstration of the syllabus coherence with the curricular unit's objectives

Topics 1 to 6 show the main techniques of problem solving by search and constraint satisfaction, and simple multi-agent systems (goal 1).
Topics 7 to 9 will show the students the basic mechanisms of machine learning and information extraction from large volumes of data (goal 2).

Demonstration of the teaching methodologies coherence with the curricular unit's objectives

Theoretical-practical classes allow the presentation, demonstration and discussion of the discipline subjects.
Practical classes will be used to solve concrete problems and follow the programming tasks execution.

relevant generic skillimproved?assessed?
Achieving practical application of theoretical knowledgeYesYes
Analytical and synthetic skillsYesYes
Balanced decision makingYesYes
Commitment to effectivenessYesYes
Creativity  
Ethical and responsible behaviour  
Event organization, planning and managementYes 
Information and learning managementYes 
IT and technology proficiencyYesYes
Problem Analysis and AssessmentYesYes
Problem-solvingYesYes
Research skillsYes 
Self-assessment  
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