Undergraduate Course: Natural Language Understanding, Generation, and Machine Translation (UG) (INFR11225)
|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
|Summary||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.
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.
Information for Visiting Students
|High Demand Course?
Course Delivery Information
|Academic year 2022/23, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
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
|Assessment (Further Info)
|Additional Information (Assessment)
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.
||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.
||Hours & Minutes
|Main Exam Diet S2 (April/May)||Natural Language Understanding, Generation, and Machine Translation (UG) (INFR11225)||2:00|
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
|There is no textbook for the course; readings will come from recent research literature.|
|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
|Course organiser||Dr Alexandra Birch-Mayne
Tel: (0131 6)50 8286
|Course secretary||Mrs Helen Tweedale
Tel: (0131 6)50 2692