Artificial Intelligence Essentials
Overview:
Introduction:
Artificial Intelligence is the ability to rationally act and think. Applied AI is achieved when one can think and act/react in daily business. Artificial Intelligence has invaded everywhere both in personal and business life. It achieves pleasing results without costing excess effort or money. It is a must now to know how wisely to manage business operations, and be able to deliver them in the most optimized and efficient fashion. In this course, the essential concepts of artificial intelligence are being demonstrated as well as how and when to employ them.
This five-day Artificial Intelligence training course has been designed to develop the insight of the participants on Artificial Intelligence. Participants will learn how to build Artificial Intelligence applications. The training modules will enable the participants to understand the theories of Artificial Intelligence and help them to understand how to solve real-world problems using a number of carefully chosen case studies.
Course Objectives:
At the end of this course the participants will be able to:
- Develop necessary AI
- Understand how to plan and analyze using logic
- Explain how to imitate human in clustering and classification
- Understand how to design a Machine Learning based applications
- Analysis 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, Budget-planner, Decision takers and Policymakers
Course Outlines:
Unit 1: An Overview of Artificial Intelligence:
- Introduction to AI and Success Stories
- Human Intelligence vs Artificial Intelligence
- History of AI
- 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
- Logic Reasoning
- Unification
- Deduction Processes
Unit 3: Machine Learning
- Supervised and Unsupervised Learning
- Classification and Clustering
- Artificial Neural Networks
- Learn by Examples
- Object Recognition
- Features and Classes
Unit 4: Fuzzy Logic
- Introduction to Fuzzy Thinking
- Fuzziness vs Probability
- Fuzzy set and Fuzzy Rules
- Importance of Fuzzy logic
- A real example of Fuzzy Controllers
- Building a Tiny Machine Learning Application
Unit 5: Genetic Algorithm
- Overview of Genetic Algorithms
- The Need for Optimization, Maximization, and Minimization
- How GA Work and Evolve
- Genetic Algorithm Chromosomes, Genes, Selection, Mutation, and Crossover
- Dimension to Use Genetic Algorithm
- Real Genetic Algorithm Examples to Optimize Business Processes