Undergraduate Course: Text Technologies for Data Science (INFR11100)

Course Outline
School	School of Informatics	College	College of Science and Engineering
Credit level (Normal year taken)	SCQF Level 11 (Year 4 Undergraduate)	Availability	Available to all students
SCQF Credits	10	ECTS Credits	5
Summary	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.
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	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.

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.

Course Delivery Information

Academic year 2014/15, Available to all students (SV1)		Quota: None
Course Start	Semester 1
Timetable	Timetable
Learning and Teaching activities (Further Info)	Total Hours: 100 ( Lecture Hours 20, Supervised Practical/Workshop/Studio Hours 4, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 72 )
Assessment (Further Info)	Written Exam 70 %, Coursework 30 %, Practical Exam 0 %
Additional Information (Assessment)	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.
Feedback	Not entered
Exam Information
Exam Diet	Paper Name		Hours & Minutes
Main Exam Diet S2 (April/May)			2:00

Academic year 2014/15, Part-year visiting students only (VV1)		Quota: None
Course Start	Semester 1
Timetable	Timetable
Learning and Teaching activities (Further Info)	Total Hours: 100 ( Lecture Hours 20, Supervised Practical/Workshop/Studio Hours 4, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 72 )
Assessment (Further Info)	Written Exam 70 %, Coursework 30 %, Practical Exam 0 %
Additional Information (Assessment)	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.
Feedback	Not entered
Exam Information
Exam Diet	Paper Name		Hours & Minutes
Main Exam Diet S1 (December)			2:00

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

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	http://course.inf.ed.ac.uk/ttsds/
Graduate Attributes and Skills	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 4164 Email: C.Edminson@ed.ac.uk