What is it?
The talk will cover the basic framework of online learning and multiarmed bandits which is a subfield of active machine learning. We will keep most part of the talk high level mainly summarizing the motivating applications, different real world problems and basic techniques (e.g. UCB and EXP3 algorithm) and their effectiveness. Only some minor discussion of rigorous proof analyses are intended to be covered. Towards the end will also talk about extensions of bandits to online prediction, portfolio optimization and other related problems. Will keep the content exciting with demos and examples.

Who is it for?
The tutorial is meant to be accessible to the entire machine learning community, and specially useful for bandits and reinforcement learning researchers. Most of the target audiences are likely to be Machine Learning oriented, cutting across grad students, postdocs, or faculties. Overall, any first year grad student is expected to be comfortable. The material intends to provide enough exposure to the audience to built a basic understanding of bandit-problems, the need of its preference counterpart, existing results, and exciting scopes of open challenges.

Prerequisites:
A basic knowledge of probability theory, and linear algebra should be enough. Familiarity to standard concentration inequalities, state of the art MAB algorithms would be helpful (only to understand the algorithm technicalities), but not necessary. The tutorial will be self contained with all the basic definitions.