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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Text Technologies for Data Science (INFR11100)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Year 4 Undergraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThe course deals with retrieval technologies behind search engines, such as Google. The course will aim to strike a balance between theoretical and system-related aspects of the field. The course will cover:

1. Theoretical aspects, including properties of text, queries, relevance, major retrieval models and evaluation;
2. System-related aspects, including crawlers, text processing, index construction and retrieval algorithms.
Course description Lectures will cover the following topics, with a typical lecture integrating material from more than one aspect.

1. Theoretical aspects:
* The nature of text, Zipf and Heaps laws, clumping
* Information needs, queries and relevance
* Evaluation of retrieval systems
* Vector-space model and latent semantic indexing
* Probabilistic model and relevance feedback
* Language models or Relevance models

2. Systems aspects:
* Search engine architecture
* Crawling and content extraction
* Text processing and representation
* Indexing methods and compression
* Distributed search and meta-search
* Dealing with vocabulary mismatch
* Duplicate detection
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser.

This course has the following mathematics prerequisites:

1. Probability theory: random variables, expectation, joint and conditional probabilities; discrete and continuous univariate distributions.

2. Algebra: definition of vectors and matrices; vector addition and inner product; matrix multiplication.

3. Calculus: functions of several variables, univariate integrals and derivatives, univariate maxima and minima.

4. Special functions: log, exp.
Information for Visiting Students
Pre-requisitesVisiting students are required to have comparable background to that
assumed by the course prerequisites listed in the Degree Regulations &
Programmes of Study. If in doubt, consult the course lecturer.
High Demand Course? Yes
Course Delivery Information
Not being delivered
Learning Outcomes
On completion of this course, the student will be able to:
  1. Describe the main algorithms for processing, storing and retrieving text.
  2. Show familiarity with theoretical aspects of IR, including the major retrieval models.
  3. Discuss the range of issues involved in building a real search engine
  4. Evaluate the effectiveness of a retrieval algorithm
Reading List
*Search Engines: Information Retrieval in Practice, W.B. Croft, D. Metzler, T. Strohman, Addison Wesley, 2008. Primary text, photocopies will be provided by instructor.
*Introduction to Information Retrieval, C.D. Manning, P. Raghavan and H. Schutze, Cambridge University Press, 2008.
*Managing Gigabytes, I.H. Witten, A. Moffat, T.C. Bell, Morgan Kaufmann, 1999.
*Information Retrieval, C. J. van Rijsbergen, Butterworths, 1979.
*Recommended Reading for IR Research Students, A. Moffat, J. Zobel, D. Hawking. SIGIR Forum, 39(2), 2005.
Additional Information
Course URL
Graduate Attributes and Skills Not entered
KeywordsNot entered
Course organiserDr Victor Lavrenko
Tel: (0131 6)51 5612
Course secretaryMrs Victoria Swann
Tel: (0131 6)51 7607
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