Undergraduate Course: Text Technologies for Data Science (INFR11145)
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  20 
ECTS Credits  10 
Summary  This course teaches the basic technologies required for text processing, focussing mainly on information retrieval and text classification. It gives a detailed overview of information retrieval and describes how search engines work. It also covers basic knowledge of the main steps for text classification.
This course is a highly practical course, where at least 50% of what is taught in the course will be implemented from scratch in course works and labs, and students are required to complete a final project in small groups. All lectures, labs, and two course works will take place in Semester 1. The final group project will be due early Semester 2 by week 3 or 4. 
Course description 
Syllabus:
* Introduction to IR and text processing, system components
* Zipf, Heaps, and other text laws
* Preprocessing: tokenization, normalisation, stemming, stopping.
* Indexing: inverted index, boolean and proximity search
* Evaluation methods and measures (e.g., precision, recall, MAP, significance testing).
* Query expansion
* IR toolkits and applications
* Ranked retrieval and learning to rank
* Text classification: feature extraction, baselines, evaluation
* Web search

Entry Requirements (not applicable to Visiting Students)
Prerequisites 

Corequisites  
Prohibited Combinations  Students MUST NOT also be taking
Text Technologies for Data Science (UG) (INFR11229)

Other requirements  MSc students must register for this course, while Undergraduate students must register for INFR11229 instead.
Maths requirements:
1. Linear algebra: Strong knowledge of vectors and matrices with all related mathematical operations (addition, multiplication, inverse, projections ... etc).
2. Probability theory: Discrete and continuous univariate random variables. Bayes rule. Expectation, variance. Univariate Gaussian distribution.
3. Calculus: Functions of several variables. Partial differentiation. Multivariate maxima and minima.
4. Special functions: Log, Exp, Ln.
Programming requirements:
1. Python and/or Perl, and good knowledge in regular expressions
2. Shell commands (cat, sort, grep, sed, ...)
3. Additional programming language could be useful for course project.
Teamwork requirement:
Final course project would be in groups of 46 students. Working in a team for the project is a requirement. 
Information for Visiting Students
Prerequisites  As above. No part time visiting students permitted. 
High Demand Course? 
Yes 
Course Delivery Information

Academic year 2024/25, Available to all students (SV1)

Quota: None 
Course Start 
Full Year 
Course Start Date 
16/09/2024 
Timetable 
Timetable 
Learning and Teaching activities (Further Info) 
Total Hours:
200
(
Lecture Hours 18,
Supervised Practical/Workshop/Studio Hours 12,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
164 )

Assessment (Further Info) 
Written Exam
30 %,
Coursework
70 %,
Practical Exam
0 %

Additional Information (Assessment) 
Exam 30%
Coursework 70%
Course Work 1 10%, individual work covers implementing basic search engine
Course Work 2 20%, individual work covering IR evaluation and web search
Course Work 3 40%, is a group project, where each group is 46 members
All of the coursework is heavy on system implementation, and thus being familiar with programming and software engineering is a prerequisite. Python is required for implementation of Course Work 1 and Course Work 2. For Course Work 3, students are free to use the implementation language they prefer. 
Feedback 
Not entered 
Exam Information 
Exam Diet 
Paper Name 
Hours & Minutes 

Main Exam Diet S2 (April/May)  Text Technologies for Data Science (INFR11145)  2:00  
Learning Outcomes
On completion of this course, the student will be able to:
 build basic search engines from scratch, and use IR tools for searching massive collections of text documents
 build feature extraction modules for text classification
 implement evaluation scripts for IR and text classification
 understand how web search engines (such as Google) work
 work effectively in a team to produce working systems

Reading List
"Introduction to Information Retrieval", C.D. Manning, P. Raghavan and H. Schutze
"Search Engines: Information Retrieval in Practice", W. Bruce Croft, Donald Metzler, Trevor Strohman
"Machine Learning in Automated Text Categorization". F Sebastiani "The Zipf Mystery"
Additional research papers and videos to be recommended during lectures 
Additional Information
Graduate Attributes and Skills 
Not entered 
Keywords  text processing,information retrieval,text classification 
Contacts
Course organiser  Dr Walid Magdy
Tel: (0131 6)51 5612
Email: wmagdy@inf.ed.ac.uk 
Course secretary  Miss Yesica Marco Azorin
Tel: (0131 6)505113
Email: ymarcoa@ed.ac.uk 

