Kaisa_2012_3_photo by Veikko Somerpuro

Enrol

Timetable

Here is the course’s teaching schedule. Check the description for possible other schedules.

DateTimeLocation
Tue 14.1.2020
14:15 - 16:00
Thu 16.1.2020
10:15 - 12:00
Tue 21.1.2020
14:15 - 16:00
Thu 23.1.2020
10:15 - 12:00
Tue 28.1.2020
14:15 - 16:00
Thu 30.1.2020
10:15 - 12:00
Tue 4.2.2020
14:15 - 16:00
Thu 6.2.2020
10:15 - 12:00
Tue 11.2.2020
14:15 - 16:00
Thu 13.2.2020
10:15 - 12:00
Tue 18.2.2020
14:15 - 16:00
Thu 20.2.2020
10:15 - 12:00
Tue 25.2.2020
14:15 - 16:00
Thu 27.2.2020
10:15 - 12:00

Other teaching

Description

Prerequisites in terms of knowledge

basic knowledge of statistics.

basic knowledge of algorithms and data structures.

Prerequisites for students in the Data Science programme, in terms of courses

Basic knowledge of machine learning.

Prerequisites for other students in terms of courses

Recommended preceding courses

Knowledge of basic HCI techniques would be an advantage, in particular user study design, experimental data collection, questionnaire design.

String Algorithms

  • knowledge of how search engines work internally, from algorithmic and systems perspectives
  • knowledge of compression methods used in search engines
  • ability to design a user study to test an information retrieval system
  • ability to analyse experimental data from user studies
  • methods and techniques to collect user search data
  • knowledge of different types of search techniques
  • knowledge of different types of user feedback
  • basic aspects of interactive information retrieval

Spring, 2020. Plan (in principle) is to offer this course every year, depending on how it is received.

The course will consist of two parts. The first part will cover the following topics:

  • inverted indexes
  • integer codes
  • basic methods for text compression
  • web crawling

The second part will cover the following topics:

  • principles of interactive information retrieval systems
  • exploratory search
  • specialist literature search and multimedia search
  • user modelling
  • user feedback
  • assessment of performance of interactive information retrieval systems
  • designing user studies to test interactive information retrieval systems
  • use of questionnaires in interactive information retrieval system assessment
  • basic machine learning methods used in interactive information retrieval systems

The grading is based on the submitted assignments/program code and the exam.

The grading scale is 1-5.

The course will be primarily provided in the form of lectures supplemented by practical sessions. Attendance is required for some of the practical sessions where students will work in groups to design a user study to test an IR system.

The course will be assessed through an exam and a group assignment.

Dorota Glowacka and Simon Puglisi.