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 Duration 21 hours

Course Outline

Introduction

Overview of Artificial Intelligence (AI)

  • Machine learning
  • Computational intelligence

Conceptual Understanding of Neural Networks

  • Generative networks
  • Deep neural networks
  • Convolutional neural networks

Exploring Various Learning Methods

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
  • Semi-supervised learning

Additional Computational Intelligence Algorithms

  • Fuzzy systems
  • Evolutionary algorithms

Investigating AI Approaches to Optimization

  • Effectively Choosing AI Approaches

Insights into Stochastic Dynamic Programming

  • Connection to AI

Applying Mechatronic Applications with AI

  • Medical applications
  • Rescue operations
  • Defense sectors
  • Industry-agnostic trends

Case Study: The Intelligent Robotic Car

Programming the Core Systems of a Robot

  • Project Planning

Implementing AI Capabilities

  • Search algorithms and motion control
  • Localization and mapping
  • Tracking and control mechanisms

Summary and Recommended Next Steps

Requirements

  • A foundational grasp of computer science and engineering principles

Target Audience

  • Engineering professionals

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