Genetic Algorithms & Neural Networks: A Practical Approach

Genetic Algorithms, Neural Networks, AI, Neuro-Evolution, Java

Description

Course Overview

Explore the cutting-edge of artificial intelligence with our detailed course on Genetic Algorithms and Neural Networks. This course is structured to take you from a theoretical understanding of complex algorithms to direct, hands-on application through a series of engaging activities and real-world problems. Perfect for those looking to deepen their AI expertise, the course covers everything from basic structures and functions to advanced applications in games and pattern recognition.

Learning Objectives

By the end of this course, students will:

1. Understand the principles and components of Genetic Algorithms, including selection, crossover, and mutation processes.
2. Gain practical experience with Genetic Algorithms by solving problems like the Traveling Salesman and function optimization.
3. Learn the basics of Neural Networks and apply them to real-world tasks such as digit recognition.
4. Develop an understanding of neuro-evolution techniques by creating a self-learning “Snake Game”.
5. Critically analyze the advantages and limitations of these AI techniques and their applications.

Target Audience

This course is designed for:

• Students and professionals interested in advanced AI technologies.
• Data scientists and engineers looking to add sophisticated algorithmic methods to their toolkits.

Course Modules

1. Theory
   • Genetic Algorithm Overview: Introduction and history.
   • Fundamentals: Basic structure, parent selection, crossover, mutation, and survivor selection.
   • Evaluation: Advantages and disadvantages of Genetic Algorithms.
2. Practical Activities with Genetic Algorithms
   • “Hello World” Introduction: Basic implementation.
   • Traveling Salesman Problem: Optimization of a classic computational problem.
   • Function Optimization: Maximizing or minimizing function values.
   • Sudoku Solver: Applying Genetic Algorithms to solve Sudoku puzzles efficiently.
3. Neural Networks Overview
   • Basics of Neural Network Architecture: Understanding layers, neurons, and activation functions.
   • Learning and Adaptation: How networks learn and evolve over time.
4. Practical Activities with Neural Networks
   • Digit Recognition: Using Neural Networks to recognize and interpret handwritten digits.
5. Advanced Application: Neuro-evolution in Games
   • Snake Game: Developing an AI that learns to play Snake using both Genetic Algorithms and Neural Networks.

Dive into the world of Genetic Algorithms and Neural Networks with our structured, practical approach that balances theory with extensive hands-on experience. Enroll today to start transforming theoretical knowledge into impactful solutions and innovations in the field of artificial intelligence.

Who this course is for:

• Students
• AI enthusiasts