Institute for Theoretical Information Technology

Fundamentals of Big Data Analytics (V2/Ü1)

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Lecture / Exercise / Exam

Contents

Prerequisites

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

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.

Lecture Notes

• Lecture 1
• Lecture 2
• Lecture 2 (Complementary Materials)
• Lecture 3
• Lecture 4
• Lecture 5
• Lecture 6
• Lecture 7
• Lecture 8
• Lecture 9
• Lecture A
• Lecture B
• Lecture C
• Lecture D
• Lecture E

Lecture Videos

• Lecture 1 :720p - 1080p
• Lecture 2 :720p - 1080p
• Lecture 3 :720p - 1080p
• Lecture 4 :720p - 1080p
• Lecture 5 :720p - 1080p
• Lecture 6 :720p - 1080p
• Lecture 7 :720p - 1080p
• Lecture 8 :720p - 1080p
• Lecture 9 :720p - 1080p
• Lecture 10 :720p - 1080p
• Lecture 11 :720p - 1080p
• Lecture 12 :720p - 1080p
• Lecture 13 :720p - 1080p
• Lecture 14 :720p - 1080p

Exercises

• Exercise 1 – Solution
• Exercise 2 – Solution
• Exercise 3 – Solution
• Exercise 4 – Solution
• Exercise 5 – Solution
• Exercise 6 – Solution
• Exercise 7 – Solution
• Exercise 8 – Solution
• Exercise 9 – Solution
• Exercise 10 – Solution
• Exercise 11 – Solution
• Exercise 12 – Solution

Datasets

• 2classPub.txt – 3classPub.txt
• 2classPubII.txt – 3classPubII.txt
• NL2classPub.txt

Help-sheet

• Help-sheet for Fundamentals of Big Data Analytics (english version only)

Pocket Calculator

While taking the written exam, you are allowed to use a pocket calculator from this list.
‘There are two problems in computer science: there’s only one joke, and it isn’t funny.’

Information about the Exam

• Review exercise (Zusatzübung) (10.03.17, 10:00AM, ICT cubes, room 002).
• The exam on Fundamentals of Big Data Analytics begins on Tuesday, March 21, 2017, 13:00 p.m. in the room 1515|001 (TEMP1) 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 are now online. The access codes are found on the information sheets distributed during the exam.
• Exam inspection (Klausur-Einsicht) (Monday, 27.03.17, 14:00h, ICT-Cubes, room 333)

Consultation hour

Prof. Dr. Rudolf Mathar nach Vereinbarung
Dr. Arash Behboodi nach Vereinbarung
Dr. Gholamreza Alirezaei nach Vereinbarung

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