Prof. Dr. Raad Bin Tareaf

Modern AI systems are powered by deep learning and advanced machine learning techniques. In this course, students will learn to build deep neural networks — with and without attention mechanisms — train models using popular AI libraries, and deploy them in the cloud. The course also introduces reinforcement learning, probabilistic graphical models, and recursive networks, giving students a strong foundation in both current and alternative AI approaches. Students will apply their skills in a final project to implement a complex AI application.

In today’s rapidly advancing technological landscape, Artificial Intelligence (AI) plays a critical role in solving complex real-world problems. This course offers students practical, hands-on experience by guiding them through the complete AI solution development pipeline — from data collection to deploying models in the cloud. Focusing on constrained, well-defined problems within limited populations (e.g., recognizing 100 individuals), students will gain deep insights into the challenges and strategies of working with real AI systems. Rather than tackling broad, open-ended recognition tasks, this course hones in on building tailored solutions where the scope is manageable but the complexity is authentic. Through teamwork, experimentation, and iteration, students will learn to bridge the gap between theoretical AI knowledge and real-world application.

Big data and cloud computing are leading edge IT technologies that team together as key enablers for today’s IT industry with its emerging AI-based solutions. In this module, students learn about the basics and advanced topics of big data and cloud computing. We look into the interplay of existing big data and cloud computing technologies, platforms and systems, their design and implementation, and their utilization in industrial and commercial applications.