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MODULE DESCRIPTOR
Module Title
Machine Learning
Reference CM2604 Version 2
Created February 2024 SCQF Level SCQF 8
Approved July 2020 SCQF Points 15
Amended April 2024 ECTS Points 7.5

Aims of Module
To provide a theoretical underpinning of a range of established machine learning (ML) algorithms with focus on implications of real-world deployment.

Learning Outcomes for Module
On completion of this module, students are expected to be able to:
1 Use a dataset for ML using data and feature engineering methods applied to a real-world data collection.
2 Practice the theory including statistical and mathematical underpinning of a range of ML algorithms.
3 Use ML evaluation methodologies to compare and contrast supervised and un-supervised ML algorithms using an established machine learning framework.
4 Adapt the ethical, social, professional and legal issues associated with collecting /creating datasets and use of machine learning models in the real-world.

Indicative Module Content
Data cleansing, missing values handling , stemming, lemming, encoding of textual data, recognition of independent / dependent variables, over fitting, under-fitting, dimensionality reduction. Supervised Learning Techniques: Regression techniques, Bayer's theorem, Naïve Bayer's, SVM, Decision Trees and Random Forest. Un-supervised Learning Techniques: Clustering, K-Means clustering, Association Mining, Apriori. Ensemble Techniques: Ada-Boost, Bagging, Stacking. Evaluation and Testing mechanisms: Precision, Recall, F-Measure, Confusion Matrices, ROC, AUC. Data Protection Act, BCS Code of conduct, Ethical Principles.

Module Delivery
The module will be delivered through a combination of lectures and tutorials. The theoretical concepts, main principles and algorithms will be introduced during the lecture and the students will be provided with exercises during the lecture to apply and test their theoretical knowledge in-class. The tutorial sessions will consist of practical exercises to apply the theoretical principles to real world problems.

Indicative Student Workload Full Time Part Time
Contact Hours 48 N/A
Non-Contact Hours 102 N/A
Placement/Work-Based Learning Experience [Notional] Hours N/A N/A
TOTAL 150 N/A
Actual Placement hours for professional, statutory or regulatory body    

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: Individual coursework covering all learning outcomes.

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 of D is required to pass the module.
Module Grade Minimum Requirements to achieve Module Grade:
A The student needs to achieve an A in C1.
B The student needs to achieve an A in C1.
C The student needs to achieve an A in C1.
D The student needs to achieve an A in C1.
E The student needs to achieve an A in C1.
F The student needs to achieve an A in C1.
NS Non-submission of work by published deadline or non-attendance for examination

Module Requirements
Prerequisites for Module CM1606, CM1601 or equivalents.
Corequisites for module None.
Precluded Modules None.

INDICATIVE BIBLIOGRAPHY
1 Geron, A. 2020. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly
2 Han, J. and Kamber, M. 2006. Data Mining: Concepts and Techniques. 2nd ed. Morgan Kaufmann.
3 Bishop, C. 2007. Pattern Recognition and Machine Learning. Springer Verlag.
4 Provost, F. and Fawcett, T. 2013. Data Science for Business. O'Reilly Media.
5 Tan, P., Steinbach, M. and and Kumbar, V. 2005. Introduction to Data Mining. Addison-Wesley

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