Here are all 41 hours of courses given in Iceland this Summer at the Machine Learning Summer School. Enjoy the rest fo your week-end. I know...you're welcome.
Introduction to Machine Learning -- Neil Lawrence (Part 1)
MLSS Iceland 2014 1:13:30
![](https://i.ytimg.com/vi/4FAZdCcj3MA/default.jpg)
What is Machine Learning: A Probabilistic Perspective -- Neil Lawrence (Part 2)
MLSS Iceland 2014 1:30:31
![](https://i.ytimg.com/vi/JuimBuvEWBg/default.jpg)
Deep Learning -- Yoshua Bengio (Part 1)
MLSS Iceland 2014 1:28:22
![](https://i.ytimg.com/vi/Fl-W7_z3w3o/default.jpg)
Deep Learning -- Yoshua Bengio (Part 2)
MLSS Iceland 2014 1:32:43
![](https://i.ytimg.com/vi/_cohR7LAgWA/default.jpg)
Deep Learning -- Yoshua Bengio (Part 3)
MLSS Iceland 2014 1:22:26
![](https://i.ytimg.com/vi/pOtvyVYAuW4/default.jpg)
Probabilistic Modelling -- Iain Murray (Part 1)
MLSS Iceland 2014 1:27:35
![](https://i.ytimg.com/vi/khagz6yWL9w/default.jpg)
Probabilistic Modelling -- Iain Murray (Part 2)
MLSS Iceland 2014 1:25:44
![](https://i.ytimg.com/vi/U1IAMQYZjfw/default.jpg)
Probabilistic Modelling -- Iain Murray (Part 3)
MLSS Iceland 2014 1:29:02
![](https://i.ytimg.com/vi/UMpY4-exFCM/default.jpg)
Big Data and Large Scale Inference -- Amr Ahmed (Part 1)
MLSS Iceland 2014 1:26:06
![](https://i.ytimg.com/vi/Mofmo6evrNA/default.jpg)
Big Data and Large Scale Inference -- Amr Ahmed (Part 2)
MLSS Iceland 2014 1:33:21
![](https://i.ytimg.com/vi/pHsuIaPbNbY/default.jpg)
Efficient Bayesian inference with Hamiltonian Monte Carlo -- Michael Betancourt (Part 1)
MLSS Iceland 2014 1:29:43
![](https://i.ytimg.com/vi/xWQpEAyI5s8/default.jpg)
Hamiltonian Monte Carlo and Stan -- Michael Betancourt (Part 2)
MLSS Iceland 2014 42:26
![](https://i.ytimg.com/vi/svXc382Y3aw/default.jpg)
Kernel methods and computational biology -- Jean-Philippe Vert (Part 1)
MLSS Iceland 2014 1:23:26
![](https://i.ytimg.com/vi/9QRVG1wB-ds/default.jpg)
Kernel methods and computational biology -- Jean-Philippe Vert (Part 2)
MLSS Iceland 2014 1:31:07
![](https://i.ytimg.com/vi/KPpFc2OASIo/default.jpg)
Kernel methods and computational biology -- Jean-Philippe Vert (Part 3)
MLSS Iceland 2014 1:38:43
![](https://i.ytimg.com/vi/6Lqt07enBGs/default.jpg)
Probabilistic Programming and Bayesian Nonparametrics -- Frank Wood (Part 1)
MLSS Iceland 2014 1:33:57
![](https://i.ytimg.com/vi/DY5yuBNEuQs/default.jpg)
Probabilistic Programming and Bayesian Nonparametrics -- Frank Wood (Part 2)
MLSS Iceland 2014 2:06:02
![](https://i.ytimg.com/vi/k2Qj0e7H9aI/default.jpg)
Probabilistic Programming and Bayesian Nonparametrics -- Frank Wood (Part 3)
MLSS Iceland 2014 25:36
![](https://i.ytimg.com/vi/17M_IeGyd_o/default.jpg)
Probabilistic Modeling and Inference at Scale -- Ralf Herbrich (Part 1)
MLSS Iceland 2014 1:23:22
![](https://i.ytimg.com/vi/A2JinCdzpRM/default.jpg)
Probabilistic Modeling and Inference at Scale -- Ralf Herbrich (Part 2)
MLSS Iceland 2014 1:37:42
![](https://i.ytimg.com/vi/6ThMzlHdKsI/default.jpg)
Submodularity and Optimization -- Jeff Bilmes (Part 1)
MLSS Iceland 2014 1:29:54
![](https://i.ytimg.com/vi/P5REi-2XSaU/default.jpg)
Submodularity and Optimization -- Jeff Bilmes (Part 2)
MLSS Iceland 2014 1:26:26
![](https://i.ytimg.com/vi/zZm2dmXTo8Y/default.jpg)
Submodularity and Optimization -- Jeff Bilmes (Part 3)
MLSS Iceland 2014 1:27:39
![](https://i.ytimg.com/vi/VJfznzL7UaM/default.jpg)
Bayesian Uncertainty Quantification for Differential Equations -- Mark Girolami (Part 1)
MLSS Iceland 2014 1:30:56
![](https://i.ytimg.com/vi/-0p1BfCy-X8/default.jpg)
Bayesian Uncertainty Quantification for Differential Equations -- Mark Girolami (Part 2)
MLSS Iceland 2014 1:32:51
![](https://i.ytimg.com/vi/ktRiDOdci54/default.jpg)
Robust Inference -- Chris Holmes (Part 1)
MLSS Iceland 2014 1:15:35
![](https://i.ytimg.com/vi/dSto9oNd_X0/default.jpg)
Robust Inference -- Chris Holmes (Part 2)
MLSS Iceland 2014 1:34:07
![](https://i.ytimg.com/vi/eETzClFHh4o/default.jpg)
Theoretical Issues in Statistical Learning -- Timo Koski (Part 1)
MLSS Iceland 2014 1:26:15
![](https://i.ytimg.com/vi/AO0nYMA20TU/default.jpg)
Theoretical Issues in Statistical Learning -- Timo Koski (Part 2)
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