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 curriculum includes four core theory courses and three core lab courses to lay strong foundations for the students. There is a wide variety of specialized electives encompassing many areas of Artificial Intelligence and data science. The MTech project requires the student to spend one year on a substantial research topic. The curriculum is available at the link.
The centre offers a dual-degree (M.Tech) program in Artificial Intelligence, Machine Learning and Applications for students of IITKGP who are currently enrolled. Interested students may apply by the end of their 6th semester. There are currently 40 seats available.
This micro-specialization comprises one foundation course on Artificial Intelligence and two electives courses related to Artificial Intelligence and Applications. The foundation course on AI introduces fundamental techniques of AI along with a gamut of real-life problems where AI techniques can be successfully applied. This course spans across different layers – knowledge representation and logic; search and reasoning frameworks; planning, learning, and communication and interaction. There is a diverse elective list that includes courses on mathematical foundations and various aspects of AI and Machine Learning and their applications.