|Type||Seminar (7 ECTS)|
|Lecturer||Dr. Jilles Vreeken|
|its-staff (at) mpi-inf.mpg.de|
Tuesdays and Thursdays, 10–12 o'clock in Room E1.7 323.
(we will not use every time slot, detailed schedule will be filled out over time)
|Max Capacity||12 students. See here how to apply.|
|Summary||In this seminar we'll be investigating the following questions: What is interesting and meaningful structure, and how can we identify this in data? What are good models for our data when we don't know about any decent priors, don't have clear expectations, or, don't even know what we're looking for? What is the ultimate model for our data and how can we approximate this model in practice? We'll be exploring these questions in light of Algorithmic Information Theory (AIT) and its practical variant, the Minimum Description Length (MDL) principle.|
|Month||Day||Type||Topic||Slides||Req. Reading||Opt. Reading|
|Oct||25||L||Practicalities, Introduction|| Ch 1.1–1.10|
|Oct||27||–||yay – no lecture – Jilles travelling|
|Nov||1||–||yay – no lecture – holiday|
|Nov||3||L||Kolmogorov Complexity|| Ch 2.1–2.2, 2.8||[2,3]|
|Nov||8||D||Kolmogorov in Action||[4,5,6]|
|Nov||10||–||yay – no lecture – Jilles travelling|
|Nov||15||L||The Minimum Description Length principle|| Ch 1|
|Nov||17||L||Coding|| Ch 1.11 &  Ch 3|
|Nov||22||L||Practical Coding 1: Matrix Factorisation||[8,9]|
|Nov||24||L||Practical Coding 2: Sequences|||
|Nov||29||D||Practical Coding 3: Graphs|||
|Dec||1||–||yay – no lecture – Jilles travelling|
|Dec||6||L||MML — model selection using priors|| Ch 17.4, |
|Dec||8||D||MDL and MML for Predictive Modeling|||
|Dec||13||–||yay – no lecture – Jilles travelling|
|Dec||15||–||yay – no lecture – Jilles travelling|
|Jan||10||L||Structure Functions||, ( Ch 17.8)|
|Jan||17||L||Information|| Ch 2.8 (1.11)|
Lecture type key:
|Mar||14||10:00||Frauke||MDL & Denoising||[16,17]|
|10:30||Magnus||Switching and Coding|||
|11:00||Dominic||Less Loss, Better Slim?||[19,20]|
|11:30||Maha||MDL & Classification|| Ch 15|
|12:00||– pizza –|
|13:00||Patrick||The Incompressibility Method|| Ch 6|
|13:30||Boris||Solomonoff's Theory of Prediction|| Ch 5.2|
|14:00||Yuliia||The Problem of Sophistication|| Ch 4|
|14:30||– break –|
|14:45||Tatiana||MDL & Linear Regression|| Ch 12|
|15:15||Nithya||Power and Perils of MDL||[22,23]|
Every student will have to give a presentation and write a report on an assigned topic. The presentation should be between 15 and 20 minutes long, and will be followed by a 10 minute discussion. The style of the presentation should be like a lecture; your fellow students can follow it with only the previous lectures of the course as background material, you should only discuss those details that are necessary, and make use of examples where possible. Topic will be assigned by the lecturer.
In addition to the presentation, students will have to hand in a report that discusses the assigned topic. Unlike the presentation, which has to be high-level, the report is where you are allowed to show off what you've learned. Summarize the material you read as clearly as you can in your own words, identifying the key contributions/most interesting or important aspects, relating the topic to any/all previous lectures in the course and/or papers read for the course, all the while correctly referring to the sources you've drawn from. The expected length of the report is 5-10 pages, but you are free to use as many pages as you like.
Students will be graded on the slides, the presentation, your answers in the discussion, and the report. Students are allowed to ask for feedback on their slides once a week, up till latest 1 week before the presentation. The provided reading material are provided as starting points. For some topics the provided materials will be (by far) (way) (more than) enough, for others you may want to gather more material. When in doubt, contact the lecturer.
The deadline for both the slides and report is right before the meeting (i.e. 10:00) in which you have to give your presentation.
All required reading will be made available here. You will need a username and password to access the papers outside the MPI network. Contact the lecturer if you don't know the username or password.
The university library kindly keeps hard copies of the two most important books for this course available in a so-called Semesteraparat.
|||An Introduction to Kolmogorov Complexity and its Applications. Springer, 1993.|
|||On Tables of Random Numbers. The Indian Journal of Statistics, Series A, 25(4):369-376, 1963.|
|||Three Approaches to the Quantitative Definition of Information. Problemy Peredachi Informatsii, 1(1):3-11, 1965.|
|||The similarity metric. IEEE Transactions on Information Technology, 50(12):3250-3264, 2004.|
|||Clustering by Compression. IEEE Transactions on Information Technology, 51(4):1523-1545, 2005.|
|||On data mining, compression and Kolmogorov Complexity. Data Mining and Knowledge Discovery, 15(1):3-20, Springer-Verlag, 2007.|
|||The Minimum Description Length Principle. MIT Press, 2007.|
|||mdl4bmf: Minimum Description Length for Boolean Matrix Factorization. ACM Transactions on Knowledge Discovery from Data, 2014.|
|||Mining Top-K Patterns from Binary Datasets in presence of Noise. In Proceedings of the 10th SIAM International Conference on Data Mining (SDM), Columbus, OH, pages 165-176, 2010.|
|||The Long and the Short of It: Summarizing Event Sequences with Serial Episodes. In Proceedings of the 18th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), Beijing, China, ACM, 2012.|
|||VoG: Summarizing Graphs using Rich Vocabularies. In Proceedings of the SIAM International Conference on Data Mining (SDM), Philadelphia, PA, SIAM, 2014.|
|||Estimation and Inference by Compact Coding. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 49(3):240-265, Wiley, 1987.|
|||Minimum Encoding Approaches for Predictive Modeling. In Proceedings of the 14th International Conference on Uncertainty in Artificial Intelligence (UAI), 1998.|
|||Kolmogorov's Structure functions and Model Selection. IEEE Transactions on Information Technology, 50(12):3265-3290, 2004.|
|||A Structure Function for Transaction Data. In Proceedings of the 11th SIAM International Conference on Data Mining (SDM), Mesa, AZ, pages 558-569, SIAM, 2011.|
|||MDL Denoising. IEEE Transactions on Information Technology, 46(7):2537-2543, IEEE, 2000.|
|||On the Behaviour of MDL Denoising. In Proceedings of AISTATS, 2005.|
|||Catching up faster by switching sooner: a predictive approach to adaptive estimation with an application to the AIC--BIC dilemma. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 74(3):361-417, Blackwell Publishing Ltd, 2012.|
|||Krimp: Mining Itemsets that Compress. Data Mining and Knowledge Discovery, 23(1):169-214, Springer, 2011.|
|||Slim: Directly Mining Descriptive Patterns. In Proceedings of the 12th SIAM International Conference on Data Mining (SDM), Anaheim, CA, pages 236-247, Society for Industrial and Applied Mathematics (SIAM), 2012.|
|||Single Sample Statistics. PhD Thesis, 2016.|
|||The Power and Perils of MDL. Proceedings of ISIT 2007, 2007.|
|||Approximation of the Two-Part MDL Code. IEEE Transactions on Information Theory, 55(1):444-457, 2009.|
In general terms, the course will consist of
The course has up to two meetings per week. The first part of the course will feature regular lectures covering the basic topics of the course and sessions in which we will discuss material covered in the lectures and scientific articles assigned by the lecturer. During the second part the students will write an essay based on scientific articles assigned to them by the lecturer and will prepare a presentation. All student presentations will be scheduled in a block-seminar style, that is in one or two days near the end of the semester, the exact date is to be announced.
There will be no weekly tutorial group meetings.
Students should have basic working knowledge of data analysis and statistics, e.g. by successfully having taken courses related to data mining, machine learning, and/or statistics, such as Topics in Algorithmic Data Analysis, Machine Learning, Probabilistic Graphical Models, Statistical Learning, Information Retrieval and Data Mining, etc.
The seminar is fully booked. It — or a course much like it — will likely be offered again in Winter Semester 2017.