Computational Intelligence

Approved in the following curricular units:

– Análise Matemática I and II;

– Programação;

– Introdução à Inteligência Artificial;

– Conhecimento e Raciocínio.

The course includes both theoretical and practical classes. The theoretical classes present methodologies and provide examples of their application to case studies. The practical classes focus on implementing algorithms and monitoring project resolution.
Students are assessed on two components: Theory (10 points) and Practice (10 points).

The theory component is assessed through a written exam.

The practical component is assessed through a three-phase project. All phases involve a presentation, defence and report writing.

This curriculum unit studies advanced concepts in computational intelligence, essentially learning mechanisms with neural networks, neurodiffuse systems, evolutionary computing paradigms, deep learning and reinforcement learning.

The main objectives are:

– Acquire knowledge in the field of Computational Intelligence;

– Apply computational intelligence techniques to real cases;

– Understand and apply advanced learning techniques;

– Knowledge and application of complex problem solving algorithms.

The main competences to be acquired are:

– Knowledge of the main computational intelligence paradigms;

– Analyse a problem, identify its characteristics and solution strategies;

– Recognise the advantages and limitations of using problem solving algorithms;

– Promote the autonomous development of new advanced problem-solving strategies.

1 Introduction to Computational Intelligence:

– Overview, Methodologies and Applications;

– Analysing real problems;

– Learning mechanisms.

2. Machine Learning.

3. Evolutionary Computation and Collective Intelligence paradigms:

– Particle Swarm Intelligence (PSO) and Variants;

– Ant Colony Optimisation.

4. Advanced Topics in Neuro-Diffuse Systems.

5. Deep-Learning.

6. Reinforcement Learning.

NAO