Core Module Information
Module title: Fundamentals of Machine Learning

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

Module code: SET08123
Module leader: Valerio Giuffrida
School School of Computing, Engineering and the Built Environment
Subject area group: Computer Science


Description of module content:

This module aims to develop the fundamental concepts of Machine Learning. At the end of this module, the students will be able to apply basic machine learning algorithms to data-driven problems. The analytic content of this module is:
• Basic concepts: the students will learn what is the main aim of ML, overfitting and underfitting, dataset splits, bias-variance dilemma.
• Mathematics: vectors, matrices, norms, determinant, matrix inverse, eigenvalues and eigenvectors, convex optimisation, derivative.
• Classifier: binary classification, linear separability, kernel trick, SVM, decision trees, random forest.
• Regression: linear regression, logistic regression, ridge regression, SVR.
• Dimensionality Reduction: PCA, ICA, Autoencoders.

Tools used in this module: Python.

Learning Outcomes for module:

LO1: Understand the basic concepts of machine learning
LO2: Understand basic concepts of linear algebra and calculus
LO3: Understand basic machine learning algorithms and their applicability
LO4: Apply machine learning algorithms to dataset and perform critical evaluation
LO5: Design, develop, and evaluate data driven applications in Python
LO6: Present the results in a scientific form and critically draw appropriate conclusions.

Full Details of Teaching and Assessment
2022/3, Trimester 1, FACE-TO-FACE, Edinburgh Napier University
Occurrence: 001
Primary mode of delivery: FACE-TO-FACE
Location of delivery: MERCHISTON
Partner: Edinburgh Napier University
Member of staff responsible for delivering module: Valerio Giuffrida
Module Organiser:

Learning, Teaching and Assessment (LTA) Approach:
Students will gain a solid understanding of the fundamental principles of Machine Learning during weekly lectures, practical labs and tutorials. These sessions will build up the students' skill in the context of machine learning, allowing them to apply machine learning algorithms to new datasets. Overall, lectures will explain the main concepts of Machine Learning (LO1,2,3,4), whereas practical sessions will be mostly focused in developing the programming skills of the students in Python (LO5) to apply the theoretical concepts using real-case examples. Tutorials will also be provided to develop further insights of the mathematical concepts of the class, allowing students to exercise concepts with practical examples (LO2,4). Lectures will be as interactive as possible, in order to better engage the students in learning the fundamentals concepts of Machine Learning. Examples of how to report results and draw appropriate conclusions provided by empirical evaluation from data will be provided to students (LO6).

Formative Assessment:
To support formative feedback, the Software Engineering subject group utilise a lab based teaching approach across their provision. During these lab sessions, staff will discuss and evaluate student progress and provide feedback on how well they are progressing with their work. All modules in the subject group also require students to demonstrate their coursework on submission to provide further formative feedback on how the work could be improved.

Summative Assessment:
Assessment will comprise a formal examination and one practical coursework. Fundamentals and theories will be tested in the exam (LOs 1, 2, 3), while the practical skills will be assessed by the coursework (LOs 4, 5, 6). Students are requested to write a coursework report using conference paper style format to present results in a scientific way.

Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)
Face To Face Lecture 24
Face To Face Tutorial 6
Face To Face Practical classes and workshops 18
Face To Face Centrally Time Tabled Examination 2
Total Study Hours50
Expected Total Study Hours for Module50

Type of Assessment Weighting % LOs covered Week due Length in Hours/Words
Practical Skills Assessment 60 4,5,6 10 HOURS= 40.00, WORDS= 4000
Centrally Time Tabled Examination 40 1,2,3 14/15 HOURS= 02.00
Component 1 subtotal: 60
Component 2 subtotal: 40
Module subtotal: 100

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