Fundamentals of Artificial Intelligence and Machine Learning (theory and exercises):

• Introduction to Artificial Intelligence (AI)
• Machine Learning (ML) - basic concepts, supervised, unsupervised, semi-supervised, and reinforcement learning, data in machine learning
• Training Models - Regression, Classification, Decision trees, Gradient Descent, K-nearest neighbors ...
• Neural Networks - Convolutional and Recurrent neural networks, and their applications in solving complex problems

AI tools for teachers (ChatGPT, MS Copilot,...)

AI/ML projects using:

• Raspberry Pi M.2 HAT+ with a Hailo AI  (AI basics: Object Detection, Image Segmentation, Pose Estimation...)
• NVIDIA Jetson Orin Nano Developer Kit (Generative AI at the Edge)
• PI-Racer Deep Learning Autopilot (Deep Learning Autopilot)
• Meta Quest 3 Unity Sentis (Building AR with AI-powered depth estimation)
• Dobot Magician (Robotic arm and computer vision using Dobot Magician)

AI tools for teachers and students (ChatGPT, MS Copilot,...)