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M.Sc.|MBA|Micro-Degree BDAI_applications-ai
Applications of AI

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.

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    10 h/week
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Applications of AI

About This Course

This course offers students a practical, hands-on experience in developing Artificial Intelligence (AI) solutions, focusing on limited-scope but complex real-world problems. Instead of recognizing faces, fingerprints, or speech across large, undefined populations, students tackle AI challenges within a constrained group (e.g., identifying 100 individuals or recognizing a small vocabulary). Through the full solution pipeline—from data capture to model development and cloud deployment—students gain deep insight into both the technical and practical aspects of AI.

Students select a focused AI problem, such as face recognition, fingerprint identification, pedestrian detection, or speech recognition with limited vocabulary, and work in small teams to develop a complete, functioning system. They will either build a new dataset or adapt existing ones, apply AI frameworks and libraries, and deploy their solutions in the cloud. The course culminates in a team presentation showcasing the working system, lessons learned, and their development journey.

By the end of the course, participants will have developed critical skills in AI model development, teamwork, project management, cloud integration, and real-world problem solving—preparing them for future careers or advanced studies in Artificial Intelligence and Machine Learning.

Requirements

Students should have basic knowledge of machine learning concepts, programming experience in Python, and familiarity with AI libraries such as TensorFlow or PyTorch. Experience with cloud platforms (AWS, Azure, or GCP) and version control tools (e.g., Git) is helpful but not required. Teamwork and communication skills are essential for successful project completion.

Course Staff

Prof. Dr. Raad Bin Tareaf

Prof. Dr. Raad Bin Tareaf

Raad Bin Tareaf is a Professor of Data Science and a researcher specializing in Artificial Intelligence, Machine Learning, and Big Data Analytics. He holds a PhD in Data Science from the Hasso Plattner Institute, where his research focused on AI-driven personality prediction models and social media analytics. He completed his Bachelor's degree in Computer Science at the German Jordanian University and earned a Master's degree in Enterprise System Engineering from Princess Sumaya University for Technology. After completing his doctorate, he transitioned into academia as a Professor of AI & Data Science. Prior to academia, he gained valuable industry experience working with companies such as Bosch and Continental GmbH. His current work focuses on bridging theoretical AI research with real-world applications, particularly in AI and deep learning.

Frequently Asked Questions

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How much programming experience do I need?

Students should be comfortable writing Python code, and have a basic understanding of machine learning concepts. Prior experience with AI libraries like TensorFlow, PyTorch, or cloud services is a plus but not mandatory.