Machine Learning (ML) is a fascinating field of artificial  intelligence (AI) research and practice where  we investigate how  computer agents can improve their perception, cognition, and action  with experience. Machine Learning is about machines improving from  data, knowledge, experience, and interaction. 

Machine learning  techniques to intelligently handle large and complex amounts of  information build upon foundations  in many disciplines, including  statistics, knowledge representation, planning and control, databases, causal inference, computer systems, machine vision, and natural  language processing.

AI agents with their core ML aim at interacting with humans in a  variety of ways, including providing estimates on phenomena, making  recommendations for decisions, and being instructed and corrected.

In our Machine Learning Department, we study and research the  theoretical foundations of the field of machine learning, as well as  on the contributions to the general intelligence goal of the field of  artificial intelligence. In addition to their theoretical education, all our students, advised by faculty, get hands-on experience with  complex real datasets.

Machine Learning can impact many applications relying on all sorts of  data, basically any data that is recorded in computers, such as health  data, scientific data, financial data, location data, weather data, energy data, etc. As our society increasingly relies on digital data, machine learning is crucial for most of our current and future  applications.