Exam Fundamentals of Big Data Analytics

Literature

Alfonso S. Bandeira, Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science.
J. Leskovec, A. Rajaraman, and J. D. Ullman, Mining of Massive Datasets Second edition. Cambridge: Cambridge University Press, 2014.
C. D. Meyer, Matrix analysis and applied linear algebra. Philadelphia: Society for Industrial and Applied Mathematics, 2000.
K. P. Murphy, Machine learning: a probabilistic perspective. Cambridge, MA: MIT Press, 2012.
C. Aggarwal, Data Mining. Cham: Springer International Publishing, 2015.
T. Hastie, R. Tibshirani, and J. H. Friedman, The elements of statistical learning: data mining, inference, and prediction, 2nd ed. New York, NY: Springer, 2009.
K. V. Mardia, J. T. Kent, and J. M. Bibby, Multivariate analysis. London ; New York: Academic Press, 1979.
Complete list of the bibliography.

There will be no lecture " Fundamentals of Big Data Analytics" this semester. The lectures and previous exercises are available at here.

While taking the written exam, you are allowed to use a pocket calculator from this list.

The reexamination of Fundamentals of Big Data Analytics begins on Monday, August 28, 2017, 10:30 in the room 1010|201. During the exam you are allowed to use a pocket-calculator that is enlisted in the list above. We will distribute the official help sheet (English version) at the beginning of the exam.
The results of the exam are now online. The access codes are found on the information sheets distributed during the exam.
Exam inspection (Klausur-Einsicht) (Monday, 04.09.17, 14:00h, ICT-Cubes, room 333)

There will be no specific preparation to these exams.