Kies de Nederlandse taal
Course module: INFOMBD
Big data
Course infoSchedule
Course codeINFOMBD
ECTS Credits7.5
Category / LevelM (M (Master))
Course typeCourse
Language of instructionEnglish
Offered byFaculty of Science; Graduate School of Natural Sciences; Graduate School of Natural Sciences;
Contact personprof. dr. A.P.J.M. Siebes
Telephone+31 30 2533229
prof. dr. A.P.J.M. Siebes
Other courses by this lecturer
Course contact
prof. dr. A.P.J.M. Siebes
Other courses by this lecturer
Teaching period
3-GS  (08/02/2021 to 23/04/2021)
Teaching period in which the course begins
Time slotD: D (WED-afternoon, Friday)
Study mode
Enrolment periodfrom 02/11/2020 up to and including 29/11/2020
Course application processOsiris Student
Enrolling through OSIRISYes
Enrolment open to students taking subsidiary coursesYes
Post-registration openfrom 25/01/2021 up to and including 22/02/2021
Waiting listYes
Course placement processadministratie onderwijsinstituut
Course goals

To pass the course you have to:

  • experiment with PAC bounds
  • write a personal (i.e., on your own) essay in which you convince the reader that you have mastered the course material

Big Data is as much a buzz word as an apt description of a real problem: the amount of data generated per day is growing faster than our processing abilities. Hence the need for algorithms and data structures which allow us, e.g., to store, retrieve and analyze vast amounts of widely varied data that streams in at high velocity.

In this course we will limit ourselves to data mining aspects of the Big Data problem, more specifically to the problem of classification in a Big Data setting. To make algorithms viable for huge amounts of data they should have low complexity, in fact it is easy to think of scenarios where only sublinear algorithms are practical. That is, algorithms that see only a (vanishingly small) part of the data: algorithms that only sample the data.

We start by studying PAC learning, where we study tight bounds to learn (simple) concepts almost always almost correctly from a sample of the data; both in the clean (no noise) and in the agnostic (allowing noise) case. The concepts we study may appear to allow only for very simple – hence, often weak – classifiers. However, the boosting theorem shows that they can represent whatever can be represented by strong classifiers.

PAC learning algorithms are based on the assumption that a data set represents only one such concept, which obviously isn’t true for almost any real data set. So, next we turn to frequent pattern mining, geared to mine all concepts from a data set. After introducing basic algorithms to compute frequent patterns, we will look at ways to speed them up by sampling using the theoretical concepts from the PAC learning framework.

Entry requirements
You must meet the following requirements
  • Assigned study entrance permit for the master
Required materials
Recommended materials
The slides completed by your own lecture notes are in principle all you need. Background reading material is, however, also available: For the first part of the course, we largely follow the first 8 chapters of the book Understanding Machine Learning, From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David you can legally download the book from a webpage of the first author you can, of course, also buy this book. It is a good book, so if you want to become a data scientist, buying it is a sensible choice For the later parts of the course we will point to the papers that the lectures are based on. You can download these papers (again legally) from anywhere in the UU network
Instructional formats


Final result
Test weight100
Minimum grade-

Kies de Nederlandse taal