Artificial Intelligence Essentials

Overview:

Introduction:

The Artificial Intelligence Essentials training program provides fundamental knowledge of AI concepts and applications. Participants explore topics like machine learning and robotics to gain core insights. Through theory and practice, they develop essential skills for applying AI across domains.

Program Objectives:

At the end of this program, participants will be able to:

• Develop a foundational understanding of AI.

• Apply logical frameworks for planning, analyzing, and decision-making in AI contexts.

• Explain how to mimic human cognitive processes in clustering and classification.

• Design machine learning-based applications.

• Analyze and design AI applications.

Targeted Audience:

• Quality, Safety, Reliability, and Security Officers.

• Project Managers.

• Executive Managers.

• Marketing Managers.

• Instrumentation, Process, Systems, Electrical, and Mechanical Engineers.

• Finance Professionals, and Policymakers.

Program Outlines:

Unit 1:

An Overview of Artificial Intelligence:

• Introduction to AI and Success Stories plus its History.

• Human Intelligence vs Artificial Intelligence.

• Intelligent Agents and Their Roles.

• Limits of Artificial Intelligence.

• Intelligent Decision-Making.

Unit 2:

Intelligent Agents:

• Introduction to Agents.

• Different Types of Agents.

• Knowledge Base and Database.

• Logical Reasoning.

• Unification.

• Deduction Processes.

Unit 3:

Machine Learning:

• Supervised and Unsupervised Learning.

• Classification and Clustering.

• Artificial Neural Networks.

• Learning by Examples.

• Object Recognition.

• Features and Classes.

Unit 4:

Fuzzy Logic:

• Introduction to Fuzzy Thinking.

• Fuzziness vs Probability.

• Fuzzy Sets and Fuzzy Rules.

• Importance of Fuzzy Logic.

• Real-World Examples of Fuzzy Controllers.

• Building a Simple Machine Learning Application.

Unit 5:

Genetic Algorithm:

• Overview of Genetic Algorithms.

• The Need for Optimization, Maximization, and Minimization.

• How Genetic Algorithms Work and Evolve.

• Genetic Algorithm Chromosomes, Genes, Selection, Mutation, and Crossover.

• Applications of Genetic Algorithms for Business Process Optimization.