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Course Outline

Introduction

Overview of Artificial Intelligence (AI)

  • Machine learning
  • Computational intelligence

Exploring Neural Network Concepts

  • Generative networks
  • Deep neural networks
  • Convolutional neural networks

Examining 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

  • Selecting AI Strategies Effectively

Understanding Stochastic Dynamic Programming

  • Its relationship with AI

Applying AI to Mechatronic Systems

  • Medicine
  • Rescue operations
  • Defense
  • Cross-industry trends

Case Study: The Intelligent Robotic Car

Programming Key Systems in Robotics

  • Project Planning

Implementing AI Features

  • Search algorithms and Motion Control
  • Localization and Mapping
  • Tracking and Control mechanisms

Summary and Recommended Next Steps

Requirements

  • Foundational knowledge in computer science and engineering

Target Audience

  • Engineers
 21 Hours

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