Prerequisites: 6.431, 18.06
Instructor: Alexander Rakhlin, email@example.com
Schedule: MW2:30-4, room 32-124
This subject counts as an Artificial Intelligence concentration subject.
In a growing number of machine learning applications---such as problems of advertisement placement, node and link prediction in evolving networks, movie recommendation---one must make online, real-time decisions and continuously improve performance with the sequential arrival of data. The course aims to provide a foundation for the development of such online learning methods and for their analysis. The course will have three interleaved components: (i) fundamental theoretical tools for analyzing online methods, (ii) algorithmic techniques for developing computationally efficient methods, and (iii) applications to real-world problems.