Algorithmic Foundations of Data Science†
Lecture in the summer term 2019
Mon, 08:30 - 10:00 h in AH V
Thu, 08:30 - 10:00 h in AH V
Fri, 14:30 - 16:00 Uhr im AH IV
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 only as a masters course.
There are no prerequisites required.†
The course will be held in english.
Time and Place
This 3-hour course will be held as 4-hour course, but not every week. The exact dates will be announced in the first lecture and can be found in RWTHonline and RWTHmoodle.
There will be weekly exercise sets. Completing these successfully, reachiong at least 50% of possible points, is necessary for admittance to the examination.
The exercise sheets will be released weekly and have to be handed in one week later. Groups of up to three students are allowed in fact, encouraged to work together and hand in the solutions together.
There will be written exams. The exact modalities of the exams will be announced later.†
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 Unpublished, draft available online.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.