Department of Information Technology

Probabilistic Machine Learning (PML) PhD course (5+3hp)

Spring 2018


Date Message
25 April 2018 In case you are interested in an alternative introduction we wrote some notes on this a while back. Have a look in Chapter 3 of these notes.
25 April 2018 During the lecture yesterday I introduced the Gaussian Process (GP). In case you want to learn more about the Gaussian process I can recommend the Gaussian Process Summer School (GPSS), more information is available here. The next instance will take place in Sheffield (UK) during 3-6 September 2018.
11 April 2018 I can recommend the NIPS 2017 Test-of-time award presentation that you can find here.
11 April 2018 For those of you with a background in physics, I find the following review quite nice.
4 April 2018 During the lecture yesterday we spoke briefly about l_p-regularized least squares. We mainly talked about the convex problems arising from p=1 and p=2, but there was also a very good question concerning the possible use of for example the l_0 norm. Here it is important to note that p=1 is a convex problem, p<1 leads to nonconvex problems. An obvious strategy is to start with norm p=1 and then initialize the nonconvex problem for the p<1 norm in the solution of the p=1 norm. Click here for some results clearly indicating that it can be interesting to consider l_p norms with p<1.
26 March 2018 The Machine Learning Summer School (MLSS) is a great event for learning machine learning and for building a network in the area. I can recommend MLSS 2018 to be held in Madrid at the end of the summer.
28 Feb. 2018 The home page is now updated for the 2018 edition of the course. Welcome!
Updated  2018-04-30 10:31:03 by Thomas Schön.