Course Details
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Course Description
This course is designed to provide participants with a comprehensive understanding of artificial intelligence (AI) and its diverse applications within the field of electrical engineering. Covering fundamental AI concepts, techniques, and real-world applications, the course aims to bridge the gap between theoretical knowledge and practical implementation
The Training Course Will Highlight ?
Training Objective
Target Audience
- Electrical engineering students and professionals.
- Researchers and academics in the field of electrical engineering.
- Engineers interested in incorporating AI into their work.
- Technologists and data scientists focusing on electrical engineering applications
Training Methods
- Presentations by the course leader, including case examples.
- Group work on case studies - analysis, interaction, and discussion.
- Hands-on sessions with AI tools and software.
- Practical projects and assignments.
Daily Agenda
DAY 1: Introduction to AI and Machine Learning
- Overview of AI: History, Definitions, and Trends
- Basic Concepts of Machine Learning
- Supervised vs. Unsupervised Learning
- AI in Electrical Engineering: An Overview
- Case Study: AI in Power Systems
DAY 2: AI Techniques and Tools
- Neural Networks and Deep Learning
- Natural Language Processing (NLP)
- Reinforcement Learning
- AI Software and Tools (TensorFlow, Keras, etc.)
- Hands-on Session: Building a Simple Neural Network
DAY 3: AI Applications in Electrical Engineering I
- AI in Power Generation and Distribution
- Smart Grids and AI
- Predictive Maintenance using AI
- Case Study: AI in Renewable Energy Systems
- Group Activity: Analyzing AI Applications in Energy Sector
DAY 4: AI Applications in Electrical Engineering II
- AI in Signal Processing
- AI in Control Systems and Automation
- Robotics and AI in Electrical Engineering
- Case Study: AI in Electrical Vehicle Technology
- Guest Lecture: Industry Expert on AI in Electrical Engineering
DAY 5: Practical Implementation and Future Trends
- Developing and Implementing AI Models
- Ethical Considerations and Challenges in AI
- Future Trends and Innovations in AI and Electrical Engineering
- Group Project Presentations
- Course Review and Q&A Session
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