AI & ML for Electrical Engineers: Theory & Hands-on

About this course

AI & ML for Electrical Engineers: Theory & Hands-onThis course provides a comprehensive introduction to Artificial Intelligence (AI) and Machine Learning (ML) with a strong focus on applications in Electrical Engineering. It is designed for students, researchers, and professionals who want to integrate intelligent techniques into modern power and energy systems.The course begins with the fundamentals of Artificial Neural Networks (ANNs), including neuron models, activation functions, architectures, and learning mechanisms. You will explore key concepts such as gradient descent, loss functions, and learning rules, building a solid theoretical foundation.Moving forward, the course covers advanced ANN paradigms and ML algorithms, including backpropagation, radial basis function networks, self-organizing maps, and functional link networks. You will also learn essential regression, clustering, and classification techniques, enabling you to analyze complex datasets effectively.A key highlight of this course is its hands-on MATLAB implementation, where you will work on real-world electrical engineering problems such as:Transformer efficiency predictionLoad forecasting and classificationBus voltage estimationFault diagnosis in electrical machinesBy the end of this course, the learners will gain expert level knowledge and will be able to design, implement, and evaluate AI/ML models for practical electrical engineering applications, making them industry-ready for smart grids and intelligent energy systems.