Bio-inspired Artificial Intelligence Algorithms

Bio-inspired Artificial Intelligence Algorithms, available at $79.99, has an average rating of 4.25, with 88 lectures, based on 146 reviews, and has 1713 subscribers.

You will learn about 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 This course is ideal for individuals who are People interested in how nature can provide inspiration for Computer Science problems or People interested in artificial intelligence algorithms, especially those inspired in Biology or Developers who want to solve real optimization and classification problems or Data Scientists who want to increase their portfolio It is particularly useful for People interested in how nature can provide inspiration for Computer Science problems or People interested in artificial intelligence algorithms, especially those inspired in Biology or Developers who want to solve real optimization and classification problems or Data Scientists who want to increase their portfolio.

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Summary

Title: Bio-inspired Artificial Intelligence Algorithms

Price: $79.99

Average Rating: 4.25

Number of Lectures: 88

Number of Published Lectures: 88

Number of Curriculum Items: 88

Number of Published Curriculum Objects: 88

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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:

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Couse content

Lecture 2: Course materials

Chapter 2: Genetic Algorithms

Lecture 1: Case study – flight schedule

Lecture 2: Creating the variables

Lecture 3: Flights dataset

Lecture 4: Printing the schedule

Lecture 5: Hours to minutes

Lecture 6: Fitness function 1

Lecture 7: Fitness function 2

Lecture 8: Genetic algorithm – intuition

Lecture 9: Part 1 – mutation

Lecture 10: Part 2 – crossover

Lecture 11: Part 3 – complete genetic algorithm

Lecture 12: Part 4 – complete genetic algorithm

Lecture 13: Part 5 – complete genetic algorithm

Chapter 3: Differential Evolution

Lecture 1: Introduction to the algorithm

Lecture 2: General structure of the algorithm

Lecture 3: The variation operator and the generation of new vectors

Lecture 4: Main differences between DE and GA

Lecture 5: Application: nutrient allocation problem

Lecture 6: Part 1 – Candidate solution

Lecture 7: Part 2 – Population of vectors

Lecture 8: Part 3 – Objective/fitness function

Lecture 9: Part 4 – selecting three other vectors

Lecture 10: Part 5 – variation operator

Lecture 11: Part 6 – selecting the best vector from each population

Lecture 12: Part 7 – running the algorithm

Lecture 13: Part 8 – solution graph

Chapter 4: Artificial Neural Networks

Lecture 1: Biological fundamentals

Lecture 2: Single layer perceptron

Lecture 3: Multi-layer networks – sum and activation functions

Lecture 4: Multi-layer networks – error calculation

Lecture 5: Gradient descent

Lecture 6: Delta parameter

Lecture 7: Adjusting the weights with backpropagation

Lecture 8: Bias, error, stochastic gradient descent, and more concepts

Lecture 9: Part 1 – digits dataset

Lecture 10: Part 2 – pre-processing the images

Lecture 11: Part 3 – training

Lecture 12: Part 4 – evaluating

Lecture 13: Part 5 – classifying one single image

Chapter 5: Clonal Selection Algorithm

Lecture 1: Clonal Selection Algorithm

Lecture 2: General structure of the algorithm

Lecture 3: Calculating the cloning factor

Lecture 4: Calculation of hypermutation

Lecture 5: Application – Digit generation/recognition

Lecture 6: Part 1 – antibody function

Lecture 7: Part 2 – antibody population

Lecture 8: Part 3 – fitness function

Lecture 9: Part 4 – antibody affinity list

Lecture 10: Part 5 – selecting the N best antibodies

Lecture 11: Part 6 – cloning the best antibodies

Lecture 12: Part 7 – Hypermutation of the antibodies

Lecture 13: Part 8 – Running the algorithm

Lecture 14: Part 9 – Solution graph

Chapter 6: Particle Swarm Optimization

Lecture 1: Introduction to the algorithm

Lecture 2: General structure of the algorithm

Lecture 3: Particles and the population (swarm)

Lecture 4: Individual best particle and Global best particle

Lecture 5: Updating the position and velocity of the particles

Lecture 6: Graphical/vectorial representation of position/velocity update

Lecture 7: Case study

Lecture 8: Part 1 – Particle

Lecture 9: Part 2 – Population

Lecture 10: Part 3 – Fitness function

Lecture 11: Part 4 – Personal best position (pbest)

Lecture 12: Part 5 – Global best position (gbest)

Lecture 13: Part 6 – Updating the position and velocity of the particle

Lecture 14: Part 7 – New position/particle

Lecture 15: Part 8 – Running the algorithm

Lecture 16: Part 9 – Solution graph

Chapter 7: Ant Colony Optimization

Lecture 1: Foraging behavior of ants

Lecture 2: Foraging behavior of ants: part 2

Lecture 3: Update of pheromone deposition

Lecture 4: Probability of edge selection

Lecture 5: Ants and the TSP problem

Lecture 6: Case study

Lecture 7: Part 1 – Edges

Lecture 8: Part 2 – Edge selection probability

Lecture 9: Part 3 – Function that chooses edges

Lecture 10: Part 4 – Generating paths/ants

Lecture 11: Part 5 – Path length function

Lecture 12: Part 6 – Pheromone update

Lecture 13: Part 7 – Running the algorithm

Lecture 14: Part 8 – 5 nodes

Lecture 15: Part 9 – Running the algorithm with 5 nodes

Chapter 8: Final remarks

Lecture 1: Final remarks

Lecture 2: BONUS

Instructors

Jones Granatyr
Professor

Guilherme Matos Passarini, phD
Professor

AI Expert Academy
Instructor

Rating Distribution

1 stars: 2 votes

2 stars: 0 votes

3 stars: 19 votes

4 stars: 46 votes

5 stars: 79 votes

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