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MODULE DESCRIPTOR
Module Title
Machine Learning and Artificial Intelligence
Reference CM3710 Version 2
Created January 2023 SCQF Level SCQF 9
Approved May 2019 SCQF Points 30
Amended June 2023 ECTS Points 15

Aims of Module
To provide students with the ability to demonstrate the practical skills required for the development of intelligent systems, including the application of machine learning, in solving real-world problems.

Learning Outcomes for Module
On completion of this module, students are expected to be able to:
1 Demonstrate a critical understanding of the use of machine learning and artificial intelligence techniques in real-world applications.
2 Critically analyse the strengths and limitations of current machine learning and Artificial Intelligence techniques.
3 Compare and contrast the main techniques within machine learning and Artificial Intelligence.
4 Develop an intelligent system using suitable machine learning and/or Artificial Intelligence techniques to solve a given problem.

Indicative Module Content
Artificial Intelligence - definition, concepts, and examples. Intelligent behaviour-Search, Case-based reasoning, Genetic Algorithms. Problem solving and intelligent search. Supervised and unsupervised machine learning including neural nets, support vector machines, decision trees, probabilistic learning, instance-based learners, metric learning and clustering algorithms. Convolutional Neural Networks and Deep Learning. Real-World Applications. Ethical AI.

Module Delivery
The module is delivered in Blended Learning mode using structured online learning materials/activities and directed study, facilitated by regular online tutor support. Workplace Mentor support and work-based learning activities will allow students to contextualise this learning to their own workplace. Face-to-face engagement occurs through annual induction sessions, employer work-site visits, and modular on-campus workshops.

Indicative Student Workload Full Time Part Time
Contact Hours 30 N/A
Non-Contact Hours 30 N/A
Placement/Work-Based Learning Experience [Notional] Hours 240 N/A
TOTAL 300 N/A
Actual Placement hours for professional, statutory or regulatory body 240  

ASSESSMENT PLAN
If a major/minor model is used and box is ticked, % weightings below are indicative only.
Component 1
Type: Coursework Weighting: 100% Outcomes Assessed: 1, 2, 3, 4
Description: This coursework will consist of an AI/machine learning development exercise and analysis.

MODULE PERFORMANCE DESCRIPTOR
Explanatory Text
The calculation of the overall grade for this module is based on 100% weighting of C1. An overall minimum grade D is required to pass the module.
Module Grade Minimum Requirements to achieve Module Grade:
A The student must achieve an A in C1.
B The student must achieve a B in C1.
C The student must achieve a C in C1.
D The student must achieve a D in C1.
E The student must achieve an E in C1.
F The student must achieve an F in C1.
NS Non-submission of work by published deadline or non-attendance for examination

Module Requirements
Prerequisites for Module None, in addition to course entry requirements.
Corequisites for module None.
Precluded Modules None.

INDICATIVE BIBLIOGRAPHY
1 RUSSELL, S. and NORVIG, P. 2016. Artificial Intelligence: A Modern Approach. 3rd ed. Pearson.
2 LEWIS, N. D. 2016, Deep Learning Step by Step with Python. CreateSpace Independent Publishing Platform.
3 RASHID T, 2016. Make Your Own Neural Network. CreateSpace Publishing.
4 FINLAY, S., 2017. Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies. Realtivistic.
5 SIEGEL, E. 2016. Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. John Wiley & Sons.

Robert Gordon University, Garthdee House, Aberdeen, AB10 7QB, Scotland, UK: a Scottish charity, registration No. SC013781