Undergraduate Course: Text Technologies for Data Science (INFR11100)

Course Outline
School	School of Informatics	College	College of Science and Engineering
Course type	Standard	Availability	Available to all students
Credit level (Normal year taken)	SCQF Level 11 (Year 4 Undergraduate)	Credits	10
Home subject area	Informatics	Other subject area	None
Course website	None	Taught in Gaelic?	No
Course description	The 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.

Entry Requirements (not applicable to Visiting Students)
Pre-requisites		Co-requisites
Prohibited Combinations	Students MUST NOT also be taking Text Technologies (Level 11) (INFR11027)	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.
Additional Costs	None

Information for Visiting Students
Pre-requisites	Visiting 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.
Displayed in Visiting Students Prospectus?	Yes

Course Delivery Information

Delivery period: 2014/15 Semester 1, Available to all students (SV1)			Learn enabled: No	Quota: None
Web Timetable	Web Timetable
Course Start Date	15/09/2014
Breakdown of Learning and Teaching activities (Further Info)	Total Hours: 100 ( Lecture Hours 20, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 76 )
Additional Notes
Breakdown of Assessment Methods (Further Info)	Written Exam 70 %, Coursework 30 %, Practical Exam 0 %
Exam Information
Exam Diet	Paper Name	Hours & Minutes
Main Exam Diet S2 (April/May)		2:00

Delivery period: 2014/15 Semester 1, Part-year visiting students only (VV1)			Learn enabled: No	Quota: None
Web Timetable	Web Timetable
Course Start Date	15/09/2014
Breakdown of Learning and Teaching activities (Further Info)	Total Hours: 100 ( Lecture Hours 20, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 76 )
Additional Notes
Breakdown of Assessment Methods (Further Info)	Written Exam 70 %, Coursework 30 %, Practical Exam 0 %
Exam Information
Exam Diet	Paper Name	Hours & Minutes
Main Exam Diet S1 (December)		2:00

Summary of Intended Learning Outcomes
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

Assessment Information
A combination of problem sets and programming exercises involving application of existing algorithms and evalution techniques. You should expect to spend approximately 24 hours on the coursework for this course. If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year.

Special Arrangements
None

Additional Information
Academic description	Not entered
Syllabus	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
Transferable skills	Not entered
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.
Study Abroad	Not entered
Study Pattern	Not entered
Keywords	Not entered

Contacts
Course organiser	Dr Victor Lavrenko Tel: (0131 6)51 5612 Email: vlavrenk@inf.ed.ac.uk	Course secretary	Miss Claire Edminson Tel: (0131 6)51 7607 Email: C.Edminson@ed.ac.uk