This course introduces the fundamental concepts and design principles of distributed computing systems. It covers key topics such as distributed system architectures, interprocess communication, remote procedure calls, and distributed shared memory. The course also explores synchronization, resource management, and fault tolerance in distributed environments. Students will gain an understanding of distributed file systems, consistency models, and modern web-based distributed applications. The course prepares learners to analyze and design scalable, reliable, and efficient distributed systems.
This course provides a comprehensive understanding of programming language, focusing on data types, control structures, functions, arrays, and pointers. It introduces concepts of dynamic memory allocation, file handling, and modular programming for developing efficient and maintainable programs. The course also covers fundamental algorithms such as searching and sorting along with their time complexity analysis. Emphasis is placed on defensive programming techniques, debugging, and writing secure and robust code. Through hands-on programming exercises, students develop problem-solving skills and practical expertise in C programming.
This course introduces the principles and practical applications of machine learning for solving real-world problems. It covers fundamental concepts such as data preprocessing, supervised and unsupervised learning, model evaluation, and feature engineering. Students learn to implement machine learning algorithms including regression, classification, clustering, and ensemble methods using modern tools and frameworks. The course also emphasizes model optimization, performance analysis, and responsible use of machine learning systems. Through hands-on projects and case studies, students gain practical experience in building and deploying machine learning models.
This course introduces the fundamental concepts and techniques of Artificial Intelligence, focusing on intelligent problem-solving and decision-making systems. It covers search strategies, knowledge representation, reasoning, and basic machine learning concepts used in AI applications. Students will explore algorithms such as uninformed and informed search, game playing, and reasoning under uncertainty. The course also provides an introduction to real-world AI applications in areas such as robotics, natural language processing, and expert systems. Through practical examples and problem-solving exercises, students develop the ability to design and analyze basic AI-based solutions.