Bio-inspired Artificial Intelligence Algorithms
What you'll learn
- Understand the theory and practice of the main bio-inspired artificial intelligence algorithms
- Solve real-world optimization problems using bio-inspired algorithms
- Minimize the price of airline tickets using Genetic Algorithms
- Create custom menus using Differential Evolution
- Classify handwritten digits using Artificial Neural Networks
- Adapt antibodies and antigens with the Clonal Selection algorithm, applied in digit recognition
- Optimize course schedules using Particle Swarm Optimization
- Solve shortest paths problems using Ant Colony Optimization
Requirements
- Programming logic
- Basic Python programming
Description
Nature offers a wide range of inspirations for biological processes to be incorporated into technology and computing. Some of these processes and patterns have been inspiring the development of algorithms that can be used to solve real-world problems. They are called bio-inspired algorithms, whose inspiration in nature allows for applications in various optimization and classification problems.
In this course, you will learn the theoretical and mainly the practical implementation of the main and mostly used bio-inspired algorithms! By the end of the course you will have all the tools you need to build artificial intelligence solutions that can be applied to your own problems! The course is divided into six sections that cover different algorithms applied in real-world case studies. See below the projects that will be implemented step by step:
Genetic Algorithms (GA): It is one of the most used and well-known bio-inspired algorithm to solve optimization problems. It is based on biological evolution in which populations of individuals evolve over generations through mutation, selection, and crossing over. We will solve the flight schedule problem and the goal is to minimize the price of air line tickets and the time spend waiting at the airport.
Differential Evolution (DE): It is also inspired in biological evolution and the case study we will solve step by step is the creation of menus, correctly balancing the amount of carbohydrates, proteins and fats.
Neural Networks (ANN): It is based on how biological neurons work and is considered one of the most modern techniques to solve complex problems, such as: chatbots, automatic translators, self driving cars, voice recognition, among many others. The case study will be the creation of a neural network for image classification.
Clonal Selection Algorithm (CSA): It is based on the functioning of the optimization of the antibody response against an antigen, resembling the process of biological evolution. These concepts will be used in practice for digit identification and digit generation.
Particle Swarm Optimization (PSO): It relies on the social behavior of animals, in which the swarm tries to find the best solution to a specific problem. The problem to be solved will be the timetable: there is a course, people who want to take it and different timetables. In the end, the algorithm will indicate the best times for each class to take the course.
Ant Colony Optimization (ACO): It is based on concepts of how ants search for food in nature. The case study will be one of the most classic in the area, which is the choice of the shortest path.
Each type of problem requires different techniques for its solution. When you understand the intuition and implementation of bio-inspired algorithms, it is easier to identify which techniques are the best to be applied in each scenario. During the course, all the code will be implemented step by step using the Python programming language! We are going to use Google Colab, so you do not have to worry about installing libraries on your machine, as everything will be developed online using Google's GPUs!
Who this course is for:
- People interested in how nature can provide inspiration for Computer Science problems
- People interested in artificial intelligence algorithms, especially those inspired in Biology
- Developers who want to solve real optimization and classification problems
- Data Scientists who want to increase their portfolio
Instructors
- 4.6 Instructor Rating
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- 180,874 Students
- 100 Courses
Olá! Meu nome é Jones Granatyr e já trabalho em torno de 10 anos com Inteligência Artificial (IA), inclusive fiz o meu mestrado e doutorado nessa área. Atualmente sou professor, pesquisador e fundador do portal IA Expert, um site com conteúdo específico sobre Inteligência Artificial. Desde que iniciei na Udemy criei vários cursos sobre diversos assuntos de IA, como por exemplo: Deep Learning, Machine Learning, Data Science, Redes Neurais Artificiais, Algoritmos Genéticos, Detecção e Reconhecimento Facial, Algoritmos de Busca, Mineração de Textos, Buscas em Textos, Mineração de Regras de Associação, Sistemas Especialistas e Sistemas de Recomendação. Os cursos são abordados em diversas linguagens de programação (Python, R e Java) e com várias ferramentas/tecnologias (tensorflow, keras, pandas, sklearn, opencv, dlib, weka, nltk, por exemplo). Meu principal objetivo é desmistificar a área de IA e ajudar profissionais de TI a entenderem como essa tecnologia pode ser utilizada na prática e que possam visualizar novas oportunidades de negócios.
- 4.6 Instructor Rating
- 1,619 Reviews
- 18,960 Students
- 22 Courses
English:
Hi, my name is Guilherme, I have a bachelor's degree in Biological Sciences, a master's degree in Experimental Biology, and a Ph.D. also in Experimental Biology, both from the Federal University of Rondônia (Brazil). My main research area is the search for compounds that are active against the parasites of malaria and leishmaniasis. I also have been programming for a while, especially in the programming languages Python and R. My main interests are biology, biotechnology, programming, medicinal chemistry, and artificial intelligence. My main goal here in Udemy is therefore spreading the knowledge related to these areas to people around the world.
Português:
Bacharel e licenciado em Ciências Biológicas pela Universidade Federal de Rondônia, mestre em Biologia Experimental pela Universidade Federal de Rondônia e doutor também em Biologia Experimental pela Universidade Federal de Rondônia. Desenvolveu seus trabalhos de iniciação científica e mestrado na busca de moléculas de plantas bioativas contra os parasitas da malária e leishmaniose, tendo trabalhado com fitoquímica e ensaios antiparasitários in vitro. No final do mestrado, começou a se interessar por bioinformática, química medicinal e programação, aplicando alguns programas de bioinformática e quimioinformática para auxiliar na descoberta de drogas antimaláricas. Possui experiência com as linguagens Python e R, e iniciou a programar em Javascript. Seu projeto de doutorado se constitui em avaliar um composto antimalárico já testado durante o mestrado de forma mais aprofundada contra o parasita da malária, realizando análises virtuais, como verificação de características físico-químicas e farmacocinéticas, docking molecular (interação virtual entre ligante e proteína-alvo do parasita) e ensaios em placas de cultura.
- 4.5 Instructor Rating
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We are an on-line platform focused on courses on Artificial Intelligence, Machine Learning and Data Science. Our goal is to offer easy-to-understand theoretical and practical content, so that professionals from all areas can understand the benefits that AI can bring to their businesses. We are established in Brazil since 2018 and we have already published more than 90 courses in English and Portuguese on the Udemy platform.