Postgraduate Course: Large Language Models: Principles and Applications (CMSE11686)
Course Outline
| School | Business School |
College | College of Arts, Humanities and Social Sciences |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course equips students with a deep understanding of how Large Language Models (LLMs) can assist data analytics and decision-making processes in various sectors. It emphasises the development of critical thinking skills necessary to analyse and evaluate the implications of LLMs in various contexts. Students will explore the fundamental concepts behind LLMs, their development, deployment, and the ethical considerations surrounding their use. This approach ensures that graduates can apply these models to solve complex business problems while engaging in informed discussions and forming well-reasoned opinions about the development and deployment of these technologies.
The course integrates the discussion of ethical and societal impacts of AI technologies, preparing students to become responsible leaders in the field. By fostering critical thinking skills, students will be able to assess the potential benefits and challenges associated with LLMs, and make informed decisions regarding their implementation in real-world scenarios. Through case studies and discussions, students will develop the ability to think critically about the role of LLMs in shaping the future of various industries and society as a whole. |
| Course description |
The course covers the foundations of Large Language Models (LLMs), including their architecture and key developments, as well as prompt engineering techniques to optimise model responses. It explores the integration of LLMs into business analytics workflows, such as anomaly detection and data interpretation. Additionally, it examines techniques for LLM customisation, including prompt tuning and fine-tuning, and introduces methods for LLM evaluation. The course also includes real-world case studies and discussions on the ethical considerations of LLM applications, such as bias, privacy, and copyright issues. Workshops provide hands-on experience in prompt engineering and LLM applications.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2025/26, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Block 3 (Sem 2) |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 10,
Seminar/Tutorial Hours 4,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
84 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
30 %,
Practical Exam
70 %
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| Additional Information (Assessment) |
70% Presentation (Group) - Assesses all course Learning Outcomes
30% Project report (Individual) - Assesses all course Learning Outcomes |
| Feedback |
Formative: Feedback will be provided throughout the course.
Summative: Feedback will be provided on assessments within agreed deadlines. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Grasp the foundational principles of artificial intelligence and machine learning, with a deep understanding of Large Language Models (LLMs), including their architecture and evolution.
- Employ prompt engineering and advanced thought models like Chain of Thoughts and Tree of Thoughts to enhance LLMs' problem-solving and reasoning capabilities.
- Identify and apply LLMs across diverse fields such as classification and anomaly detection, recognising their role in augmenting human tasks and improving efficiency.
- Master prompt tuning and fine-tuning techniques to customise LLM outputs for specific applications, evaluating the effectiveness of these methodologies in various scenarios
- Analyse real-world case studies to understand the development and application of LLM-based solutions, alongside an awareness of the ethical considerations surrounding the use of LLMs in technology.
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Reading List
Core text
Prompt Engineering for Generative AI by James Phoenix, Mike Taylor. Publisher(s): O'Reilly Media, Inc. |
Additional Information
| Graduate Attributes and Skills |
Practice: Applied Knowledge, Skills and Understanding
After completing this course, students should be able to:
Apply creative, innovative, entrepreneurial, sustainable and responsible business solutions to address social, economic and environmental global challenges.
Work with a variety of organisations, their stakeholders, and the communities they serve - learning from them, and aiding them to achieve responsible, sustainable and enterprising solutions to complex problems.
Communication, ICT, and Numeracy Skills
After completing this course, students should be able to:
Critically evaluate and present digital and other sources, research methods, data and information; discern their limitations, accuracy, validity, reliability and suitability; and apply responsibly in a wide variety of organisational contexts.
Cognitive Skills
After completing this course, students should be able to:
Be self-motivated; curious; show initiative; set, achieve and surpass goals; as well as demonstrating adaptability, capable of handling complexity and ambiguity, with a willingness to learn; as well as being able to demonstrate the use digital and other tools to carry out tasks effectively, productively, and with attention to quality.
Knowledge and Understanding
After completing this course, students should be able to:
Demonstrate a thorough knowledge and understanding of contemporary organisational disciplines; comprehend the role of business within the contemporary world; and critically evaluate and synthesise primary and secondary research and sources of evidence in order to make, and present, well informed and transparent organisation-related decisions, which have a positive global impact.
Identify, define and analyse theoretical and applied business and management problems, and develop approaches, informed by an understanding of appropriate quantitative and/or qualitative techniques, to explore and solve them responsibly. |
| Keywords | Large Language Models,Predictive Analysis,Digital Technology |
Contacts
| Course organiser | Dr Tong Wang
Tel: (0131 6)51 5551
Email: Tong.Wang@ed.ac.uk |
Course secretary | |
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