Fundamentals of Big Data Analytics (V2/Ü1)

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Links to Campus Office

Basic course on probability and stochastic processes as given, e.g., in Theoretische Informationstechnik I. Bachelor courses on mathematics.

Lecture notes for Fundamentals of Big Data Analytics
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.

The lectures from winter semester 2016/2017 have been recorded and are published online.

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

Review exercise (Zusatzübung), Thursday 01.03.2018 10:00AM, at room 333.
The exam on Fundamentals of Big Data Analytics begins on Monday, March 12, 2018, 14:00 p.m. in the room 1010|131 (AachenMünchener Halle (Aula)) and lasts 90 minutes. 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 will be published on Friday evening, the 16.03.18, on the homepage of the institute.
Exam inspection (Klausur-Einsicht) (Friday, 23.03.18, 10:00h, ICT-Cubes, room 333)