20130625 The students' seminar work is available at https://github.com/bayesianinference. 20130422 Unprocessed video recording of the lectures are available at http://www.youtube.com/playlist?list=PLrM7Z8xNORRdvGS6qEkbNmXavtutAEEeG. All recorded slides are available at http://www.youtube.com/playlist?list=PLrM7Z8xNORRdiuN6TjWDAfh4g633XdWGl. 20130422 Dear students, based on the last doodle, we will meet on Friday the 26th at 9:00am. I have decided that the most important is to go through the examples presented during the guest lectures. As a result, I believe that each of you should present at least one solution to a example on a whiteboard in front of the rest of the colleagues. The presentation will include description of the problem, detailed derivation and presentation of the implementation of the problem on computer, ideally implemented in Python, without using high level libraries such as PyGP, or PyMC. NumPy or SciPy is fine. Details will be discussed this Friday. There is the list of examples and each of you can signup for one or more examples (alternatives) you are interested in solving. Corresponding doodle is available at: http://doodle.com/bg4v9tqis74i6mz5
We will determine exactly who will present which example on the Friday's meeting. We will start with the presentations the next week. 20130419Added photos collected during lectures. 20130418 Uploaded the final version of slides for the seventh and eight lecture. Uploaded the slides recording for the remaining lectures. The video recording of the lectures will be postedited and later posted on this web page. I would like to thank to our guest speakers for their wonderful talks and the insight into Bayesian machine learning they shared with us. Now, when the guest lectures finished, we have to agree on how to proceed further. I prepared a doodle to determine the next suitable lecture date and time: http://doodle.com/925pb32mhhatukun Please tell me when you are available. 20130416 Uploaded the final version of slides for the sixth lecture. 20130415 Uploaded the final version of slides for the fifth lecture. 20130412 Uploaded a preliminary version of the last lecture on the topic of Dirichlet Processes. 20130411 Please note that there is also an online course called Probabilistic Graphical Models available at https://www.coursera.org/course/pgm. This course can help you to understand some of the topics presented in this course. Peter Cerno, thanks for bringing this up. 20130411 Uploaded the final version of slides for the fourth lecture. Today's talk is about Expectation Propagation. 20130410 Uploaded the final version of the third lecture slides and preliminary slides for the lectures 57. 20130409 Uploaded the final version of the second lecture slides. 20130408 The guest lectures started. Uploaded final version of the first lecture slides and the accompanying exercise. 20130405 Added lecture topics and slides for the first four lectures. The first four lectures will be presented by Jose Miguel HernandezLobato. 20130404 The guest lectures starts the next week on Monday at 9:00am at the MS S1 lecture room. 20130228 Added lectures dates and times. 20130228 An introductory series of video lectures on Bayesian inference can be found at http://videolectures.net/mlss2012_lapalma/ . 20130219 I will organise an informal introduction for the prospective students of this course on this Friday (20130222) at 2pm in the S1 lecture room. I will discuss the goals and content of the course and how it complements other courses taught at MFF. All are welcome. 20130215 Even if you do not want to officially participate in the course, you are still welcome. Also, you can attend only the lectures presented by the experts from Cambridge if it suits you better. Just to make allocation of lecture rooms easier, could you please respond to the following doodle: http://doodle.com/vu6ttns2ugt3fnk8 ? 20130214 The course is in SIS as NPFL108  Bayesian inference so that you can sign up. 20130207 Literature update. Added a link to the 4F13 Machine Learning course taught by C. Rasmussen and Z. Ghahramani at Cambridge.
20130204 Syllabus update.
20130204 The start date of the lectures was set to the week beginning on 8.4.2013.
The course aims to provide students with basic understanding of modern Bayesian inference methods. The course will emphasize and discuss methods which have application in robotics, natural language processing, data mining, web search. The course will be composed of a series of lectures presented by experts from the Machine Learning Group, Cambridge University, UK, led by Prof. Zoubin Ghahramani. The presenters will be José Miguel Hernández Lobato and Sara Wade. The guests will present 8 lectures during two weeks. The exact dates of the lectures will be specified later. The lectures will begin with the week staring on 8.4.2013. After these two intensive weeks, there will several practicals (laboratory exercise) which should help the students implement the some of the presented methods. The course will be close by an examination. Before examination students must submit solved examples form practicals. The course will be presented in English and it will be based on the the machine learning course 4F13 taught by Carl Edward Rasmussen and Zoubin Ghahramani at Cambridge University Engineering Department. The following link includes slides from this course as well as the practicals that the students are expected to do: http://mlg.eng.cam.ac.uk/teaching/4f13/1213/ If you are interested in the subject and want to sign up, then you can do it here or simply indicate your interest at http://doodle.com/vu6ttns2ugt3fnk8 .
Lectures dates and timesThere will be 8 lectures. The lectures are planned for early morning so that they do not conflict with your other activities.
SyllabusLecture
topics:
LicenseThe slides, video recording, and photos are released under the Creative Commons Attribution–ShareAlike License 3.0 (CCBYSA 3.0).LiteratureThe material covered in the lectures can be found in recent text books:
[2] C. M. Bishop: Pattern Recognition and Machine Learning, vol. 4, no. 4. Springer, 2006, p. 738. [3] K. Murphy: Machine Learning: a Probabilistic Perspective, the MIT Press (2012). [4] D. Barber: Bayesian Reasoning and Machine Learning, Cambridge University Press (2012), available freely on the web. [5] D. MacKay: Information Theory, Inference, and Learning Algorithms, Cambridge University Press (2003), available freely on the web at http://www.inference.phy.cam.ac.uk/mackay/itila/. It is also include videolectures. [6] C. Rasmussen, Z. Ghahramani: 4F13 Machine Learning taught at Cambridge University Engineering Department. Slides and the practicals: http://mlg.eng.cam.ac.uk/teaching/4f13/1213/
