In the Machine Learning Tutorial Series, external guest speakers will give tutorial lectures on focused machine learning topics. The target audience are undergraduates, MSc and PhD students, post-docs and interested faculty members.
All talks will be announced via the ml-talks mailing list.
If you are looking for previous tutorials, check out the ML Tutorials Archive.
Normally, the talks will be on Wednesdays, 14:00 – 16:00.
|2018-11-28||Dino Sejdinovic (University of Oxford)||Approximate Kernel Methods, Embeddings, and Aggregates|
|2018-11-07||Tor Lattimore (DeepMind)||Bandit algorithms and Reinforcement learning|
Bandit algorithms and Reinforcement learning (Tor Lattimore, 2018-11-07)
Decision making in the face of uncertainty is a significant challenge in machine learning. Which drugs should a patient receive? How should I allocate my study time between courses? Which version of a website will return the most revenue? What move should be considered next when playing chess/go? All of these questions can be expressed in the multi-armed bandit framework where a learning agent sequentially takes actions, observes rewards and aims to maximise the total reward over a period of time. The framework is now very popular, used in practice by big companies, and growing fast. The focus of the tutorial will be on understanding the statistical ideas, mathematics and implementation details of the core algorithmic concepts.