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 tools for teachers and students (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)

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

Fundamentos de Inteligência Artificial e Aprendizado de Máquina (teoria e exercícios)

• Introdução à Inteligência Artificial (IA)
• Aprendizado de Máquina (ML) – conceitos básicos, aprendizado supervisionado, não supervisionado, semissupervisionado e por reforço, dados no aprendizado de máquina
• Treinamento de Modelos – Regressão, Classificação, Árvores de decisão, Gradiente descendente (Gradient Descent), K-vizinhos mais próximos (K-nearest neighbors)...
• Redes Neurais – Redes neurais convolucionais e recorrentes, e suas aplicações na resolução de problemas complexos

Základy umelej inteligencie a strojového učenia (teória a cvičenia)

• Úvod do umelej inteligencie (AI)
• Strojové učenie (ML) – základné koncepty, učenie s učiteľom (supervised), bez učiteľa (unsupervised), čiastočne kontrolované (semi-supervised) a posilňované učenie (reinforcement learning), dáta v strojovom učení
• Trénovanie modelov – regresia, klasifikácia, rozhodovacie stromy, gradientný zostup (Gradient Descent), K-najbližších susedov (K-nearest neighbors)...
• Neurónové siete – konvolučné a rekurentné neurónové siete a ich aplikácie pri riešení zložitých problémov