COURSE OVERVIEW

 This practical and highly-interactive course includes real-life case studies where participants will be engaged in a series of interactive small groups and class workshops.

This course is designed to provide participants with a detailed and up-to-date overview of Artificial Intelligence (AI) and Machine Learning. It covers the differences between AI, ML and deep learning and its applications in engineering fields; the machine learning fundamentals, mathematical foundations for ML, data preparation and engineering and AI tools and platforms; the AI use cases in mechanical engineering covering predictive maintenance in rotating machinery, fault detection in pumps, compressors and turbines, energy optimization in HVAC and manufacturing and smart robotics and automation; the regression techniques, classification algorithms, model training and evaluation and applications in mechanical systems; the clustering techniques and dimensionality reduction; and the anomaly detection methods. During this interactive course, participants will learn the reinforcement learning basics comprising of RL concepts and agents, Q-learning fundamentals, deep Q networks (DQN) and applications in robotics and control systems; the neural networks basics, activation functions and backpropagation, overfitting and regularization in deep learning and applications in image and signal processing; the deep neural networks (DNNs), computer vision applications, natural language processing (NLP) basics and AI in simulation and digital twins; the AI ethics and safety in engineering and AI for process optimization; the IoT and AI integration, edge versus cloud AI in manufacturing, cyberphysical systems and AI-enabled production lines; the AIpowered materials discovery, AI in additive manufacturing (3D printing) and autonomous maintenance robots; and the AI for sustainable engineering.