Program

Lecture(r)s

Below you will find, probably by August, short abstracts of the lectures together with an indication of useful prerequisites. Clicking on the photos gets you to the respective lecturers' homepage.

Prabhakar Raghavan Verity, Inc. / Stanford University Elements of Text and Web Search		Abstract: These lectures will cover the basics of inverted indexes to handle text querying and scoring of retrieved results. Students are expected to be familiar with basic concepts from data structures, linear algebra and probability. Online Resources: Here is a pdf version of the slides of lecture 1, lecture 2, and lecture 3.
Thomas Hofmann Brown University Matrix Decomposition Techniques in Information Retrieval and Machine Learning		Abstract: The tutorial discusses various methods from statistics and machine learning that are based on matrix decompositions. This includes classical methods such as principal component analysis and factor analysis, but also more recent achievements such as non-linear PCA, independent component analysis, non-negative matrix factorization, (probabilistic) latent semantic analysis and spectral clustering. The lectures will not deal with numerical issues of matrix decompositions, but rather illustrate how such methods can be used in the context of machine learning and its applications. Special emphasis is put on tasks from the domain of information retrieval such as semantic search, collaborative filtering and hyperlink analysis. Prerequisites: Some basic knowledge in linear algebra, probability and statistics. Online Resources: Here is a pdf version of the slides.
Soumen Chakrabarti Indian Institute of Technology, Bombay Using Graphs in Unstructured and Semistructured Data Mining		Abstract: Until recently, machine learning and data mining techniques focused on single, flat tables of feature vectors. However, in the last few years, there has been an explosion of data mining applications where the underlying data has an irresistible graphical interpretation. The Web is a standard example by now, but there are many other domains spanning the physical Internet, emails, USENET, blogs, keyword search in XML and relational data, multi-relational mining, natural language processing, and biological data. This short course will give a concise overview of the important concepts and tools for characterizing, modeling, and analyzing graph structures common in some of the application domains listed above, with pointers to active research areas, latest publications, and available software. Prerequisites: For data management researchers and professionals who need to deal with data domains naturally represented by graphs; algorithm designers. Basic (undergraduate level) probability and systems performance maturity is assumed. Online resources: Soumen has posted under his homepage an up-to-date version of his slides, some exercises & solutions, and a reading list.

Organization