COURSE CURRICULUM

In Semester 1, students acquire strong theoretical and practical foundations in artificial intelligence. Machine Learning introduces core supervised and unsupervised learning techniques and their real-world applications. Data Protection and Cryptography focuses on securing data and ensuring privacy in AI systems. Generative Methods explores modern generative models and their applications in content creation and data synthesis. Parallel Computing develops skills in high-performance computation necessary for training large-scale AI models. AI Programming emphasizes practical implementation using contemporary programming languages and frameworks. Autonomous Vehicles examines intelligent control systems and perception technologies used in self-driving solutions. Efficient Data Visualization trains students to communicate complex analytical results effectively. Cloud Computing introduces scalable AI deployment environments, while Reinforcement Learning focuses on adaptive, decision-making algorithms.In Semester 2, the program advances toward specialized and applied topics. Image Processing Based on Neural Networks covers deep learning approaches for computer vision tasks. Natural Language Processing and Text Mining addresses language understanding, information extraction, and conversational AI systems. AI Frameworks provides hands-on experience with widely used development platforms. Big Data Handling Techniques explores large-scale data storage and distributed processing solutions. The program culminates in Thesis Work, where students demonstrate independent problem-solving, development, and research skills in a selected AI-related field.