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: Kehinde Babaagba
School School of Computing, Engineering and the Built Environment
Subject area group: Computer Science
Prerequisites

Requisites: Pre-requisite: Scripting for Data 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:

Upon completion of this module you will be able to

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
2024/5, Trimester 1, In Person,
VIEW FULL DETAILS
Occurrence: 001
Primary mode of delivery: In Person
Location of delivery: MERCHISTON
Partner:
Member of staff responsible for delivering module: Kehinde Babaagba
Module Organiser:


Student Activity (Notional Equivalent Study Hours (NESH))
Mode of activityLearning & Teaching ActivityNESH (Study Hours)NESH Description
Face To Face Lecture 20 This will provide lecture-based teaching with interactive components, introducing students to the foundational concepts, algorithms and applications of machine learning providing both theoretical understanding and practical context.
Face To Face Tutorial 4 These tutorials on the fundamentals of machine learning will focus on the mathematical foundations, particularly linear algebra and calculus and will include hands-on problem solving, guided derivations, and practical examples linking mathematical concepts to core machine learning algorithms.
Face To Face Practical classes and workshops 14 This will provide practical classes focused on applying theoretical knowledge to real -word problems, fostering critical thinking and technical proficiency in machine learning fundamentals. This will include hands-on activities where students implement algorithms, analyze datasets and explore concepts like regression, classification and clustering using programming tools.
Face To Face Centrally Time Tabled Examination 2 The examination will provide a means to assess the fundamental machine learning concepts covered throughout the module.
Online Guided independent study 160 This involves students exploring key machine learning concepts through curated readings, exercises and practical coding tasks. These will encourage self-paced learning while being supported by periodic lecturer feedback and structured guidance.
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 60 4~5~6 Week 10 HOURS= 4000 words This will assess students’ ability to apply machine learning algorithms to a dataset, critically evaluate the results, and design, develop, and assess data-driven applications using Python. The assessment will require students to present their findings in a scientific format and draw well-reasoned, critical conclusions based on their analysis.
Centrally Time Tabled Examination 40 1~2~3 Exam Period HOURS= 2 Centrally Time Tabled Examination
Component 1 subtotal: 60
Component 2 subtotal: 40
Module subtotal: 100

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