B.Sc.

In this course, students learn the fundamentals of artificial intelligence—its history, goals, and core definitions—while exploring key techniques such as supervised, unsupervised, and reinforcement machine learning, deep‑learning neural networks (including CNNs and RNNs), natural‑language processing for text and sentiment analysis, and computer‑vision methods for image processing and object detection. They also gain practical experience with Python programming and essential math concepts like linear algebra and calculus, and they examine real‑world AI applications such as chatbots, recommendation systems, and autonomous vehicles.