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.