Undergraduate Course: Advanced Topics in Natural Language Processing (UG) (INFR11288)
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 follows the delivery and assessment of Advanced Topics in Natural Language Processing (INFR11287) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11287 instead. |
| Course description |
This course follows the delivery and assessment of Advanced Topics in Natural Language Processing (INFR11287) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11287 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 | |
| Prohibited Combinations | |
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 Degree Programme Table (DPT), please seek special permission from the course organiser.
Maths requirements:
Linear algebra: Vectors: scalar (dot) product, transpose, unit vectors, vector length and orthogonality. Matrices: addition, matrix multiplication. Tensors with more than 2 axes.
Special functions: properties and combination rules for logarithm and exponential.
Calculus: Rules for differentiating standard functions (including chain rule), partial derivative.
Probability theory: Discrete univariate and multivariate random variables. Expectation and variance. Joint and conditional distributions.
Programming requirements:
Students should be familiar with programming in a modern object-oriented language, ideally Python which is the course language |
Information for Visiting Students
| Pre-requisites | Maths requirements:
Linear algebra: Vectors: scalar (dot) product, transpose, unit vectors, vector length and orthogonality. Matrices: addition, matrix multiplication. Tensors with more than 2 axes.
Special functions: properties and combination rules for logarithm and exponential.
Calculus: Rules for differentiating standard functions (including chain rule), partial derivative.
Probability theory: Discrete univariate and multivariate random variables. Expectation and variance. Joint and conditional distributions.
Programming requirements:
Students should be familiar with programming in a modern object-oriented language, ideally Python which is the course language
This is a fourth-year honours level course; students are expected to have an academic profile equivalent to the first three years of this degree programme. Study equivalent to the following University of Edinburgh courses is required: Accelerated Natural Language Processing (INFR11125) OR Foundations of Natural Language Processing (INFR10078) |
Course Delivery Information
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| Academic year 2025/26, Available to all students (SV1)
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Quota: None |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 29,
Supervised Practical/Workshop/Studio Hours 4,
Summative Assessment Hours 2,
Revision Session Hours 1,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
160 )
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| Assessment (Further Info) |
Written Exam
70 %,
Coursework
30 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Written Exam 70%«br /»
Coursework 30%«br /»
One semester-long practical coursework with programming and a written report. |
| 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. |
| No Exam Information |
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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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. |
| Keywords | ATNLP,NLU,NLU+,Natural Language Processing,Computational Linguistics,Generative AI |
Contacts
| Course organiser | Dr Edoardo Ponti
Tel: (0131 6)51 1336
Email: eponti@ed.ac.uk |
Course secretary | Miss Kerry Fernie
Tel: (0131 6)50 5194
Email: kerry.fernie@ed.ac.uk |
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