THE UNIVERSITY of EDINBURGH

DEGREE REGULATIONS & PROGRAMMES OF STUDY 2022/2023

Timetable information in the Course Catalogue may be subject to change.

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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Natural Language Understanding, Generation, and Machine Translation (UG) (INFR11225)

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 Credits20 ECTS Credits10
SummaryThis course follows the delivery and assessment of Natural Language Understanding, Generation, and Machine Translation (INFR11157) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11157 instead.
Course description This course follows the delivery and assessment of Natural Language Understanding, Generation, and Machine Translation (INFR11157) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11157 instead.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Accelerated Natural Language Processing (INFR11125) OR Foundations of Natural Language Processing (INFR10078)
Co-requisites Students MUST also take: Machine Learning and Pattern Recognition (INFR11130) OR Applied Machine Learning (INFR11211) OR Machine Learning (INFR10086) OR Machine Learning Practical (UG) (INFR11223)
Prohibited Combinations Students MUST NOT also be taking Natural Language Understanding, Generation, and Machine Translation (INFR11157)
Other requirements This course follows the delivery and assessment of Natural Language Understanding, Generation, and Machine Translation (INFR11157) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11157 instead.

Programming skills are required.

This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your Degree Programme Table (DPT), please seek special permission from the course organiser.
Information for Visiting Students
Pre-requisitesAs above.
High Demand Course? Yes
Course Delivery Information
Academic year 2022/23, Available to all students (SV1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 30, Seminar/Tutorial Hours 6, Supervised Practical/Workshop/Studio Hours 6, Feedback/Feedforward Hours 2, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 150 )
Assessment (Further Info) Written Exam 60 %, Coursework 40 %, Practical Exam 0 %
Additional Information (Assessment) 60% Exam
40% Coursework

The coursework component of the assessment will consist of two longer assignments in which parts of an NLP system will be implemented and the results analyzed.
Feedback Tutorials will be devoted to discussing questions, including some exam-like questions, and providing feedback on student answers. Students will also get feedback on their work through labs, through formative comments on coursework submissions, and through online discussion.
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)Natural Language Understanding, Generation, and Machine Translation (UG) (INFR11225)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Identify and discuss the main linguistic, machine learning, and ethical challenges involved in the development and use of natural language processing systems
  2. Understand and describe state-of-the-art models and algorithms used to address challenges in natural language processing systems
  3. Design, implement, and apply modifications to state-of-the-art natural language processing systems
  4. Understand the computational and engineering challenges that arise in the use of different models for natural language processing, and discuss the pros and cons of different models for a given task
  5. Understand, design and justify approaches to evaluation and error analysis in natural language processing systems
Reading List
There is no textbook for the course; readings will come from recent research literature.
Additional Information
Graduate Attributes and Skills Students will develop their skills in reading research papers and identifying pros and cons of different approaches. They will also learn to analyse and discuss results from their own implementations.
Keywordsnatural language processing,NLU+,machine translation
Contacts
Course organiserDr Alexandra Birch-Mayne
Tel: (0131 6)50 8286
Email: a.birch@ed.ac.uk
Course secretaryMrs Helen Tweedale
Tel: (0131 6)50 3827
Email: Helen.Tweedale@ed.ac.uk
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