Masterclass: Machine Learning Theory Part 1; Probability Approximately Correct (PAC) Learning
Speaker: Varun Kanade, University of Oxford, Turing Faculty Fellow
Date: 13 March 2017
This masterclass is no longer being livestreamed. We apologise for the inconvenience.
Recordings will be made available on our YouTube channel following the event.
In this class Varun Kanade will cover the probably approximately correct (PAC) learning framework. He will talk about sample complexity and computational complexity of learning algorithms and understand how we may prove bounds about this. Time permitting he will also discuss some of support vector machines, kernels, or boosting.
If you have any questions about this event, please email the Turing Academic Programmes team at email@example.com