Lectures on Intelligent Systems
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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.
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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).
