Lectures on Intelligent Systems

This textbook provides the reader with an essential understanding of computational methods for intelligent systems. These are defined as systems that can solve problems autonomously, in particular problems where algorithmic solutions are inconceivable for humans or not practically executable by computers. Despite the rapidly growing applications in this field, the book avoids application details, instead focusing on computational methods that equip the reader with the methodological tools and competencies necessary to tackle current and future complex applications.

The book consists of two parts: computational intelligence methods for optimization, and machine learning. Part I begins with the concept of optimization, and introduces local search algorithms, genetic algorithms, and particle swarm optimization. Part II begins with an introduction to machine learning and covers several methods, many of which can be used as supervised learning algorithms, such as decision treelearning, artificial neural networks, genetic programming, Bayesian learning, support vector machines, and ensemble methods, plus a discussion of unsupervised learning.

This textbook is written in a self-contained style, suitable for undergraduate or graduate students in computer science and engineering, and for self-study by researchers and practitioners.

Provides the reader with an essential understanding of intelligent systems Does not describe applications and instead focuses on computational methods Discusses optimization problems and machine learning problems

Autorentext

Leonardo Vanneschi is a Full Professor at the Nova Information Management School (NOVA IMS) of the Universidade Nova de Lisboa, Portugal. His main research interests involve machine learning, data science, optimization, complex systems and, in particular, evolutionary computation. He has published more than 200 contributions, 11 of which have been recognized with international awards. In 2015, he received the Evo* Award for Outstanding Contribution to Evolutionary Computation in Europe. In 2020, he was included in the list of the top 2% world researchers in a study carried out by Stanford University. Sara Silva is a Principal Investigator at the Computer Science and Engineering Research Centre (LASIGE) of the Universidade de Lisboa, Portugal. Her main research interests are machine learning and evolutionary computation, including interdisciplinary applications in the areas of remote sensing and bioinformatics. She is the author of around 100 peer-reviewed publications, having received more than 10 nominations and awards for best paper and best researcher. In 2018 she received the Evo* Award for Outstanding Contribution to Evolutionary Computation in Europe. She created the MATLAB Genetic Programming Toolbox (GPLAB).

Inhalt
Chapter 1: Introduction.- Chapter 2: Optimization Problems and Local Search.- Chapter 3: Genetic Algorithms.- Chapter 4: Particle Swarm Optimization.- Chapter 5: Introduction to Machine Learning.- Chapter 6: Decision Tree Learning.- Chapter 7: Artificial Neural Networks.- Chapter 8: Genetic Programming.- Bayesian Learning.- Chapter 10: Support Vector Machines.- Chapter 11: Ensemble Methods.- Chapter 12: Unsupervised Learning.