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.
Core Courses
Theory: Foundations of Machine Learning, Algorithmic and Mathematical Foundations of Artificial Intelligence, Deep Learning Foundations and Applications, Artificial Intelligence Foundations and Applications.
Labs: Machine Learning Laboratory, Data Engineering Laboratory, Deep Learning Laboratory
Elective courses (not exhaustive)
Linear Algebra for AI/ML, Statistical Foundations of AI/ML, Artificial Intelligence for Manufacturing, Artificial Intelligence for Economics, Graph Machine Learning: Foundations and Applications, Visual Computing with AI/ML, Interpretable Machine Learning, AI/ML for Robot Autonomy, Graphical and Generative Models for Machine Learning, Machine Learning for Earth System Sciences, Big Data Processing, Secure and Dependable AI/ML, Knowledge Modeling and Semantic Web Technologies, Advanced Learning Paradigms in AI, Artificial Intelligence for Cyber-Physical Systems
Areas of research
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.
