Bayesian Learning
Description
Bayes Rule provides a simple and powerful framework for machine learning. This tutorial will be organised as follows: 1. I will give motivation for the Bayesian framework from the point of view of rational coherent inference, and highlight the important role of the marginal likelihood in Bayesian Occam's Razor. 2. I will discuss the question of how one should choose a sensible prior. When Bayesian methods fail it is often because no thought has gone into choosing a reasonable prior. 3. Bayesian inference usually involves solving high dimensional integrals and sums. I will give an overview of numerical approximation techniques (e.g. Laplace, BIC, variational bounds, MCMC, EP...). 4. I will talk about more recent work in non-parametric Bayesian inference such as Gaussian processes (i.e. Bayesian kernel "machines"), Dirichlet process mixtures, etc.
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why video stops after 1 min , I can't see more .
The video stops after a minute.
Yes, same here. I also tried playing to RealVideo (instead of Windows media player) and found the same problem again.
thanks to Zoubin and you but i have same problems with viewing it.
thanks
Mostafa
That's not really a player problem but people preparing this streaming... We are wasting time here.... Shame, was looking for lecture on this.
Just drag it a bit and it works... :)
The lecture is most interesting and insightful. I am a PhD student in Lisbon University and the presentation on bayesian methods helped me to come across not only the big picture underpinning these methods, but also some very important details and possibel applications.
Thank you Professor Gharahmani
Very nice and inspiring lecture. I'll check out the other talks from Professor Gharahmani, because he is an amazing lecturer. Thanks a lot!