The B.Tech. in AI course offers a comprehensive education in Artificial Intelligence, covering theoretical, software, systems, and application aspects. It strikes a good balance between fundamental and application-oriented subjects. The core courses provide a robust foundation in both the theory and practice of Artificial Intelligence, complemented by strong laboratory components that enhance the theoretical learning. The program offers a wide variety of elective courses in areas such as language processing, complex systems and social networks, computer vision, generative and graphical models, information retrieval, and embedded machine learning. These electives cover many recent advances in the field, including Large Language Models and Generative AI, which are highly relevant to a wide range of industries. Additionally, there are electives that explore the impact of AI on various sectors like health, education, and agriculture. As the field of AI progresses rapidly, the courses are continually monitored and updated, with new electives frequently added. This new B.Tech has been introduced as part of the institute's concerted effort to advance AI education and research.
The B. Tech. in Artificial Intelligence is a blend of engineering foundations, computing and mathematical foundations, basic and advanced artificial intelligence courses. The curriculum also offers a bouquet of electives covering application of AI in multiple domains like agriculture, healthcare, robotics etc. The curriculum has been designed with the following guiding principles:
Foundation Courses: Programming and data structures, Physics, Mechanics, Calculus and Linear Algebra, Probability & Statistics, Engineering drawing, Data science, Electronics
Basic AI Courses: Artificial intelligence, Machine learning, Algorithms, Database management systems, Deep leaning, Optimization techniques, Information retrieval, natural language processing
Emerging Topics in AI: Reinforcement Learning, Signal processing of Artificial Intelligence, Statistical foundations of AI, Big data analytics, Responsible and trustworthy AI, Interpretable machine learning, Generative AI, AI for Robotics, Graph machine learning, Visual computing, Semantic web, LLM and Information retrieval
AI for X: Machine learning for earth system science, AI for cyber-physical system, AI for economics, AI for manufacturing etc.
Artificial Intelligence and Machine Learning, Knowledge Representation and Reasoning, Information Retrieval, Robotics, Trustworthy AI, Natural Language Processing, Computer Vision and Image Processing, Complex Networks, Social Computing, Cyber-Physical Systems, Safe Learning, Robotics