Information Retrieval and Data Mining

Core course, 9 ECTS credits, winter semester 2017 – 2018

News

  • 2017-09-01: more information will follow soon

Basic Information

Type

  • Core course, 9 ECTS credits

Lecturers 

Coordinators

Teaching Assistants

Time & Location

The first lecture is on Wednesday, October 18, 2017.

Tutorials

  • Monday, 14-16
  • Tuesday, 10-12

First tutorials will be on October 23 and October 24, 2017. There will be several tutorial groups at these times. More details will be announced soon.

Contact:

Course Contents

Information Retrieval (IR) and Data Mining (DM) are methodologies for organizing, searching and analyzing digital contents from the web, social media and enterprises as well as multivariate datasets in these contexts. IR models and algorithms include text indexing, query processing, search result ranking, and information extraction for semantic search. DM models and algorithms include pattern mining, rule mining, classification and recommendation. Both fields build on mathematical foundations from the areas of linear algebra, graph theory, and probability and statistics.

Prerequisites

Good knowledge of undergraduate mathematics (linear algebra, probability theory) and basic algorithms.

Literature

We will use the following primary textbooks.

For Probability and Statistics,

  • Larry Wasserman: All of Statistics, Springer, 2004

For Data Mining,

  • Charu Aggarwal: Data Mining - The Textbook, Springer, 2015

For Information Retrieval,

  • Chris Manning, Prabhakar Raghavan, Hinrich Schütze: Introduction to Information Retrieval, Cambridge, 2008
  • ChengXiang Zhai, Sean Massung: Text Data Management and Analytics, Morgan Claypool, 2016

We will list other interesting and potentially useful books later.