Undergraduate Course: Natural Language Understanding, Generation, and Machine Translation (UG) (INFR11225)
This course will be closed from 31 July 2025
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 | Following the closure of this course, a suggested replacement for students to consider is: Advanced Topics in Natural Language Processing (UG) INFR11288.
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. |
| 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.
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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)
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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)
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| Prohibited Combinations | Students MUST NOT also be taking
Natural Language Understanding, Generation, and Machine Translation (INFR11157)
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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.
As an MSc-level course that assumes previous experience with NLP, it will discuss a range of different issues, including linguistic / representational capacity, computational efficiency, optimisation, etc.
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-requisites | As above. |
| High Demand Course? |
Yes |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- identify and discuss the main linguistic, machine learning, and ethical challenges involved in the development and use of natural language processing systems
- understand and describe state-of-the-art models and algorithms used to address challenges in natural language processing systems
- design, implement, and apply modifications to state-of-the-art natural language processing systems
- 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
- understand, design and justify approaches to evaluation and error analysis in natural language processing systems
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Reading List
| There is no textbook for the course; readings will come from recent research literature. |
Additional Information
| Course URL |
https://opencourse.inf.ed.ac.uk/nlu-11 |
| 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. |
| Keywords | natural language processing,NLU+,machine translation |
Contacts
| Course organiser | Dr Alexandra Birch-Mayne
Tel: (0131 6)50 8286
Email: a.birch@ed.ac.uk |
Course secretary | Miss Kerry Fernie
Tel: (0131 6)50 5194
Email: kerry.fernie@ed.ac.uk |
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