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What do self-driving cars, face recognition, web search, industrial robots, missile guidance, and tumor detection have in common?

They are all complex real world problems being solved with applications of intelligence (AI).

This course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems.

You will learn about the history of AI, intelligent agents, state-space problem representations, uninformed and heuristic search, game playing, logical agents, and constraint satisfaction problems.

Hands on experience will be gained by building a basic search agent. Adversarial search will be explored through the creation of a game and an introduction to machine learning includes work on linear regression.

 

What you’ll learn

  • Introduction to Artificial Intelligence and intelligent agents, history of Artificial Intelligence
  • Building intelligent agents (search, games, logic, constraint satisfaction problems)
  • Machine Learning algorithms
  • Applications of AI (Natural Language Processing, Robotics/Vision)
  • Solving real AI problems through programming with Python

Perquisites: Machine Learning Diploma

Program Duration: 80 hours

Program Language: English / Arabic

Location: EPSILON TRAINING INSTITUTE | Head Office

 

Participants will be granted a completion certificate from Epsilon Training Institute, USA if they attend a minimum of 80 percent of the direct contact hours of the Program and after fulfilling program requirements (passing both Final Exam and Project to obtain the Certificate)

 

Course Syllabus

1: Introduction to AI, history of AI, course logistics
2: Intelligent agents, uninformed search
3: Heuristic search, A* algorithm
4: Adversarial search, games
5: Constraint Satisfaction Problems
6: Machine Learning: Basic concepts, linear models, perceptron, K nearest neighbors
7: Machine Learning: advanced models, neural networks, SVMs, decision trees and unsupervised learning
8: Markov decision processes and reinforcement learning
9: Logical Agent, propositional logic and first order logic
10: AI applications (NLP)
11: AI applications (Vision/Robotics)
12: Review and Conclusion

Course Curriculum

Introduction to AI, history of AI, course logistics
Intelligent agents, uninformed search
Heuristic search, A* algorithm
Adversarial search, games
Constraint Satisfaction Problems
Machine Learning: Basic concepts, linear models, perceptron, K nearest neighbors
Machine Learning: advanced models, neural networks, SVMs, decision trees and unsupervised learning
Markov decision processes and reinforcement learning
Logical Agent, propositional logic and first order logic
AI applications (NLP)
AI applications (Vision/Robotics)
Review and Conclusion

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