KNOWLEDGE, INTERACTION AND INTELLIGENT SYSTEMS-1

This course is part of the PhD program in Computer Science. It aims at investigating the state of the art methods and algorithms in the area of Computational Intelligence, with a focus on recent advancements in evolutionary computation, swarm intelligence, multi-objective optimization, genetic programming, and fuzzy reasoning.

The course has the following learning outcomes:
Knowledge and understanding: understanding the most advanced computational intelligence methods
Applying knowledge and understanding: being able to apply CI algorithms to complex research problems, or to design novel algorithms
Making judgements: being able to analyze different methods and compare them with state of the art solutions

Basic notions of optimization and linear algebra. Knowledge of Python. Foundations of ML.

Computational Intelligence is the "theory, design, application and development of biologically and linguistically motivated computational paradigms" (definition by IEEE). Traditionally, the three main pillars of CI have been Neural Networks, Fuzzy Systems and Evolutionary Computation. This course will elaborate on the last two pillars. Specifically, the topics of the course include:
- Evolutionary Computation (advanced versions of genetic algorithms, evolution strategies, and differential evolution)
- Swarm Intelligence (particle swarm optimization, ant colony optimization)
- Multi-objective optimization (domination, NSGA-II, NSGA-III, OMOPSO)
- Genetic Programming (GP, CGP, AutoML)
- Fuzzy Logic and interpretable AI

Scientific papers; lecture notes.
Optional book: Vanneschi and Silva, "Lectures on Intelligent Systems", 1st ed., Springer, 2023.

Project at the end of the course. Application of CI to an open research problem, or design of a novel CI method.

Theoretical lessons describing the various concepts and methods; active learning and open discussions.

English

oral