Fundamentals of Big Data Analytics (V2/Ü1)
The information on this website is also available in the Lehr- und Lernportal der RWTH Aachen. Every participant should sign up there.
Links to RWTH Online
Contact
Please reach out to us via bigdata@ti.rwth-aachen.de, regarding this lecture, the exercises, the exam or anything else concerning this course.
Prerequisites
Basic course on probability and stochastic processes as given, e.g., in Theoretische Informationstechnik I. Bachelor courses on mathematics.
Literature
- 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.
- Brandon Rohrer, How Support Vector Machines work
- Complete list of the bibliography.
Lecture Notes
- Lecture 1 (2018-10-12)
- Lecture 2 (2018-10-19)
- Lecture 3 (2018-10-26)
- Lecture 4 (2018-11-02) –(Bivariate Normal Distribution)
- Lecture 5 (2018-11-09)
- Lecture 6 (2018-11-16)
- Lecture 7 (2018-11-23)
- Lecture 8 (2018-11-30)
- Lecture 9 (2018-12-07)
- Lecture 10 (2018-12-21)
- Lecture 11 (2019-01-11)
- Lecture 12 (2019-01-18)
- Lecture 13 (2019-01-25)
- Lecture 14 (2019-02-01)
Lecture Videos
The lectures from winter semester 2016/2017 have been recorded and are published online.
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
- Exercise 13 – Solution
- Exam Sample – Solution
Programming Exercises
- Fundamentals of Big Data Analytics- Programming Exercises
- Introduction to MNIST dataset (Ipython)
- Dimensionality Reduction (Ipython)
- Classification and Clustering (Ipython)
- Support Vector Machines (Ipython)
Datasets
- MNIST dataset in CSV format: MNIST_trains.csv – MNIST_test.csv
- 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.
Information about the Exam
- Review exercise (Zusatzübung), Thursday 28.02.2019 10:00AM at ICT cubes, room 333.
- The exam on Fundamentals of Big Data Analytics begins on Thursday, March 7, 2019, 10:30 a.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 Thursday evening, the 14.03.19, on the homepage of the institute.
- Exam inspection (Klausur-Einsicht) (Monday, 18.03.19, 10:00h, ICT cubes, room 333)
Consultation hour
Prof. Dr. Rudolf Mathar nach Vereinbarung
Dr. Arash Behboodi nach Vereinbarung
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