ARTIFICIAL INTELLIGENCE COURSE

60 hours 0 Enrolled No ratings yet All Levels

ARTIFICIAL INTELLIGENCE COURSE OUTLINE

Artificial Intelligence one year AI, machine learning, and deep learning program designed by industrial and academic experts based on practical and hands-on learning.

What you’ll learn

  1. Discuss AI models and areas of application.
  2. Elaborate upon different models for knowledge representation.
  3. Explain the fundamentals of expert systems and apply them to problem solving.
  4. Use graph theory and finite state machines to represent problems.
  5. Develop AI solutions for problem solving using heuristic strategies.
  6. program AI systems using Prolog or Lis
  7. in this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks.
  8. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI.
  9. You will also demonstrate AI in action with a mini project
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What's included

  • 1. Introduction to Artificial Intelligence
  • 2. Overview of main AI topics
  • 3. Artificial Intelligence and Predicate Calculus.
  • 4. AI Programming Languages: Prolog and Lisp.
  • 5. Graph Theory and Strategies for State Space Searches.
  • 6. Heuristic Search Algorithms
  • 7. Control and Implementation of State Space Searches
  • 8. Knowledge Representation
  • 9. Expert Systems and Problem Solving:
  • 10. Introduction to Machine Learning
  • 11. Wide spectrum of subjects (rather than just a few in detail)
  • 12. Methodology and tools for problem solving
  • 13. Outlook at AI research
  • 14. Propaedeutic to applications and advanced courses in problem solving, decision systems, planning, robotics, data mining, collective intelligence, etc.
  • 15. Introduction: History and foundations of AI
  • 16. Problem solving:
  • 17. Uninformed and informed Search
  • 18. Constraint Satisfaction Problems and Constrained Optimization problems (complete and incomplete techniques)
  • 19. Adversarial Search: two players games, games with uncertainty
  • 20. Decision support systems and technologies
  • 21. Knowledge representation
  • 22. Reasoning
  • 23. Expert systems Contents
  • 24. Planning (basics)
  • 25. Machine learning Basics of:
  • 26. Decision trees
  • 27. Ensemble learning
  • 28. Reinforcement learning
  • 29. Evolutionary computation
  • 30. Neural networks

What Will I Learn?

  • Discuss AI models and areas of application.
  • Elaborate upon different models for knowledge representation.
  • Explain the fundamentals of expert systems and apply them to problem solving.
  • Use graph theory and finite state machines to represent problems.
  • Develop AI solutions for problem solving using heuristic strategies.
  • program AI systems using Prolog or Lis
  • in this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks.
  • You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI.
  • You will also demonstrate AI in action with a mini project

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About Trainer

EC-Council Certified Ethical Hacker

AWS Certified Solutions Architect by Amazon

Teacher Demo

Digital Dubai

Tech Fest AI Summit 2023

Artificial intelligence and data science

AI summit at NICL National Incubation Center

GoogleForEducation

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