Foundations of Data Science
Lecture in the winter term 2016/2017
In the age of "big data" and "advanced analytics", data processing faces new challenges. Queries become more complex and often involve data mining and machine learning tasks, and the scale of the datasets requires new algorithmic approaches.
This course will cover the theoretical foundations of modern data processing and analytics. This includes topics from database theory, such as data models, the analysis of query languages, and basic algorithmic and complexity theoretic questions related to query processing. It also includes topics from algorithmic learning theory, such as basic machine learning algorithms, support vector machines, the PAC model, and VC-Dimension. Furthermore, it includes new models of computation on massive datasets, such as the streaming model and the map-reduce paradigm, and algorithms for these models.
We will focus on computational aspects of the theory. Statistics, though undoubtedly one of the foundations of data science, will not play a central role in this course.
This lecture can be taken as a bachelor or master course.
There are no prerequisites required.
The course will be held in English.
There are written exams. The exact modalities of the exams are announced in L2P. The exam dates are:
Thursday, Februrary 15, 2018, 11:30am, 2350|111 (AH II)
Thursday, March 22, 2018, 11:30am, 2350|009 (AH I)
S. Abiteboul, R. Hull, V. Vianu. Foundations of Databases. Addison Wesley 1995.
J. Hopcroft, R. Kannan. Foundations of Data Science. Unpublished, draft available online.
M. Kearns, U. Vazirani. An Introduction to Computational Learning Theory. MIT Press 1994.
J. Leskovec, A. Rajaraman, J. Ullman. Mining of Massive Datasets. Cambridge University Press 2014.
S.J. Russell, P. Norvig. Artificial Intelligence: A Modern Approach. 3rd Edition, Pearson 2014.