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What you would learn in Bio-inspired Artificial Intelligence Algorithms course?
Nature has a broad range of ideas for biological processes that can be integrated into technology and computer. Sure these patterns and processes have influenced the creation of algorithms that could tackle real-world issues. They are known as bio-inspired algorithms based on nature and permit applications to different classification and optimization problems.
To help you understand this field, In this course, you will be taught the theory and, most importantly, the implementation of the most widely utilized bio-inspired algorithms! At the end of the course, you'll have all the necessary tools to develop artificial intelligence-based solutions that can apply to your issues! The course is split into six parts that will provide different algorithms used in real-world cases. Check out the projects below, which will be carried out in a step-by-step manner:
Genetic Algorithms (GA): Among the most widely used and well-known bio-inspired algorithms to tackle optimization issues. Based on the evolution of the biological system where individuals' populations develop over generations by mutation and selection and crossing over. The goal is to resolve the problem of the flight schedule, and our goal is to reduce the cost of tickets to the airport and the time spent waiting in the airport.
Digital Evolution (DE): It also has its roots in biological evolution. The problem we'll solve step-by-step is the development of menus, properly balancing the number of proteins, carbohydrates, and fats.
Neural Networks (ANN): It is based on the way that biological neurons function and is considered to be one of the latest methods for solving complex problems, like chatbots, automatic translators, self-driving vehicles' voice recognition, and numerous others. The focus of the case study is the development of a neural network used for image classification.
Clonal Selection algorithm (CSA): It is based on the operation of optimizing the response of the antibody to an antigen. It is akin to the process of evolution in biology. These concepts can be applied in the real world for digit identification and generation of numbers.
Particle Swarm Optimization (PSO): It's based on the behavior of social animals, and the swarm is trying to determine the most effective solution to a particular issue. The issue to be resolved is the timetable. There's a class, people who wish to attend it, and various timetables. The algorithm will suggest the best time for each class to take the course.
Ant Colony Optimization (ACO): It is based on how ants seek food in the natural world. The study is one of the most famous on the subject and is the one that chooses the most efficient route.
Every kind of issue will require different strategies for its resolution. If you know the logic and application for the bioinspired algorithm, it will be simpler to determine which strategies are most suitable for each situation. In the course, all code will be developed step-by-step using this Python programming language! We will use Google Colab, which means that you won't need to be concerned about installing any libraries on your computer because all the code will be created on Google's GPUs!
Content of the Course:
Know the theory and the practice of the principal artificial intelligence algorithms based on bio-inspired design.
Find solutions to real-world optimization issues with bio-inspired algorithms.
Reduce the cost of airline tickets by using Genetic Algorithms
Make custom menus using Differential Evolution
Classify handwritten digits using Artificial Neural Networks
The ability to adapt antibodies and antigens is facilitated by antigens and antibodies using the Clonal Selection algorithm, applied to the process of digit recognition.
Optimize course schedules using Particle Swarm Optimization
Find the shortest path to solve problems with Ant Colony Optimization
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