Core Module Information
Module title: Artificial Intelligence

SCQF level: 08:
SCQF credit value: 20.00
ECTS credit value: 10

Module code: SET08825
Module leader: Ben Paechter
School School of Computing, Engineering and the Built Environment
Subject area group: Computer Science
Prerequisites

There are no pre-requisites for this module to be added

Description of module content:

The module is partly based on the first three sections of “Artificial Intelligence: A Modern Approach” (4th edition) by Russell and Norvig. The module covers the history of AI and traditional AI approaches to solving problems as well as covering the technologies behind more recent advances such as Large Language Models. The module introduces you to the Transformer architecture behind Large Language Models (LLMs). You will learn how to use LLMs well by use of advanced prompting techniques, and how to fine-tune LLM’s for particular tasks. You will also learn how to evaluate LLMs and consider the ethical issues in their use. The use of the “human in the loop” to improve these kinds if AI systems through reinforcement learning is also considered.Looking at more tradition approaches to AI, the concept of an intelligent agent is explored along with the architectures that might support intelligent agent systems.Traditional search techniques such as those used within pathfinding software such as Google Maps are explored and compared. Techniques for searching where there is an opponent or “adversary” – for example when playing chess, are also explained. We also look at ways in which these search techniques can be optimised.Other traditional approaches such as knowledge representation, propositional logic, and reasoning are covered providing insights into AI approaches that are “explainable”. You will also learn about simple ways to solve optimisation problems that have many constraints – for example automated timetable production, and some more advanced methods that are inspired by nature.

Learning Outcomes for module:

Upon completion of this module you will be able to

LO1: Develop insight into how AI concepts can underpin problem solving tasks.

LO2: Utilise appropriate artificial intelligence techniques to solve example problems including making use of appropriate software tools and libraries.

LO3: Compare, contrast and discuss AI based solutions for specific problems

LO4: Describe and develop insight into ethical issues related to AI

LO5: Comprehend and explain a variety of AI approaches

Full Details of Teaching and Assessment
2024/5, Trimester 2, Online (fully o,
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Occurrence: 001
Primary mode of delivery: Online (fully o
Location of delivery: WORLDWIDE
Partner:
Member of staff responsible for delivering module: Ben Paechter
Module Organiser:


Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)NESH Description
Face To Face Lecture 10 You are expected to watch a number of short (around 20 minute) recorded lecture style videos by the Module team and also other online material. At the one hour in person session each week you can ask questions about this material.
Independent Learning Centrally Time Tabled Examination 2 Centrally Time Tabled Examination
Online Guided independent study 188 Guided independent study
Total Study Hours200
Expected Total Study Hours for Module200


Assessment
Type of Assessment Weighting % LOs covered Week due Length in Hours/Words Description
Practical Skills Assessment 30 2~3 Week 10 HOURS= 16 hours Practical Skills Assessment. The assessment is split into two main parts - each providing practical tasks to demonstrate the application of AI techniques to solve problems making appropriate choices in doing so. These assessments will vary from year to year but will be founded in: (1) LLM prompt construction, tuning and optimisation (2) classical search techniques and comparisons.
Centrally Time Tabled Examination 60 1~2~3~4~5 Exam Period HOURS= 2 Centrally Time Tabled Examination. Students do 3 questions from 5 possible. All equal marks.
Class Test 10 1~2~3~4~5 Week 11 HOURS= 1 hour each Ten end of unit tests. Each has ten multiple choice questions. Each test is about the material is the unit just covered.
Component 1 subtotal: 40
Component 2 subtotal: 60
Module subtotal: 100

Indicative References and Reading List - URL:
Contact your module leader