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Artificial Intelligence

Objectives

To provide the students with the historical and current context of Artificial Intelligence. To teach the functioning of a wide array of Artificial Intelligence methods, from the most classical techniques of search and reasoning to the most cutting-edge methods of machine learning. To raise awareness to the issue of ethics in Artificial Intelligence.

General characterization

Code

100101

Credits

6.0

Responsible teacher

Sara Guilherme Oliveira da Silva

Hours

Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

Prerequisites

None. This is an introductory course, with a wide range of contents, but not very deep, in particular on the most advanced methods. No programming skills required. The mathematics used is extremely basic.

Bibliography

The WWWW (Wonderful World Wide Web).

Teaching method

Lessons include:
   - Theoretical explanations (on the white board)
   - Demos (on the computer)
   - Exercises (on paper)
   - Usage of some software (on the computer)
   - Tutorials by invited lecturers

In the classroom, pen and paper are MANDATORY!

There will be very few PDF materials. Students MUST attend classes.

Evaluation method

Open-book mini-tests during the semester (40%). Closed-book written exam in the end of the semester (60%).

Subject matter

Introduction
   - Presentation of the course
   - History and fiction of AI
   - Definition and main concepts

Knowledge Representation and Reasoning
   - Goal trees
   - Facts and rules
   - Inference
   - Prolog
   - Uncertainty: Fuzzy and Probabilistic Inference Systems
   - Ontologies and Semantic Web

Search
   - Basic search
   - Heuristic search
   - Optimal search
   - State space and dynamic environments
   - Search with constraints
   - Evaluation of heuristics

Games
   - Minimax algorithm
   - Minimax with Alpha-Beta
   - Iterative deepening

Machine Learning
   - Introduction, motivation and definition
   - Hierarquical clustering and K-Means
   - K-Nearest Neighbors
   - Decision Trees and Random Forests
   - Neural Networks: Multilayer Perceptron, Deep Learning
   - Support Vector Machines
   - Evolutionary Computation: Genetic Algorithms and Genetic Programming

Complex Systems

Ethics in AI (transversal topic)