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  5. SCNU Joint Institute Computing Science
  6. JC3509: Machine Learning

JC3509: MACHINE LEARNING (2026-2027)

Last modified: 10 Oct 2025 12:16


Course Overview

This course introduces the foundations of Machine Learning. It covers core algorithms like regression, classification and gradient descent before advancing to deep learning, including Neural Networks, CNNs, and RNNs. The curriculum examines advanced paradigms such as reinforcement learning and foundation models, building essential skills in model training and validation for a rigorous understanding of the field.

Course Details

Study Type Undergraduate Level 3
Term Second Term Credit Points 15 credits (7.5 ECTS credits)
Campus Offshore Sustained Study No
Co-ordinators
  • Mr Aiden Durrant

What courses & programmes must have been taken before this course?

  • Any Undergraduate Programme (Studied)

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

None.

Are there a limited number of places available?

No

Course Description

This course provides a comprehensive and foundational introduction to the field of Machine Learning (ML). The curriculum is structured to build a rigorous understanding of the principles behind training and validating machine learning models, starting from fundamental algorithms and progressing to the advanced architectures that define modern artificial intelligence. As a core component of the program, this course ensures students develop the essential theoretical knowledge and practical skills required in this domain.

The course curriculum is organised into the following modules:

  •  Foundations of ML: The course begins with a formal introduction to machine learning. It covers the prediction of continuous values using Linear Regression and binary outcomes with Logistic Regression. The core optimisation algorithm, Gradient Descent, is detailed, along with regularisation techniques for preventing model overfitting.
  • Core Modelling and Evaluation: Following the fundamentals, you will learn the essential techniques for building and validating models. It includes a study of various Classification methods, industry-standard Model Assessment and Validation strategies, and the structure of Decision Trees.
  • Deep Learning: The curriculum starts with the fundamentals of Neural Networks and progresses to specialised architectures, including Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for sequential data. The Attention Mechanism is also a key topic where you will conclude this section with an analysis of Transformers, large-scale Foundation Models, and their impact on the field.
  • Advanced Paradigms: Extending to other major areas of machine learning you will then cover methods for discovering latent structure in data through Unsupervised and Self-Supervised Learning and the principles of decision-making in Reinforcement Learning.
  • Application of ML: Finally, you will examine real-world applications of ML and reflect via a comprehensive recap to consolidate the key concepts covered throughout the course.

Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

More Information about Week Numbers


Details, including assessments, may be subject to change until 31 August 2025 for 1st Term courses and 19 December 2025 for 2nd Term courses.

Summative Assessments

Assignment

Assessment Type Summative Weighting 30
Assessment Weeks Feedback Weeks

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
ProceduralAnalyseAbility to identify, prepare, and manage appropriate datasets for analysis.
ProceduralApplyAbility to appropriately present the results of data analysis.
ProceduralEvaluateAbility to analyse the results of data analyses, and to evaluate the performance of analytic techniques in context.
ProceduralEvaluateKnowledge and understanding of analytic techniques, and ability to appropriately apply them in context, making correct judgements about how this needs to be done.

Exam

Assessment Type Summative Weighting 70
Assessment Weeks Feedback Weeks

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
ProceduralAnalyseAbility to identify, prepare, and manage appropriate datasets for analysis.
ProceduralApplyAbility to appropriately present the results of data analysis.
ProceduralEvaluateAbility to analyse the results of data analyses, and to evaluate the performance of analytic techniques in context.
ProceduralEvaluateKnowledge and understanding of analytic techniques, and ability to appropriately apply them in context, making correct judgements about how this needs to be done.

Formative Assessment

There are no assessments for this course.

Resit Assessments

Exam

Assessment Type Summative Weighting 100
Assessment Weeks Feedback Weeks

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Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Course Learning Outcomes

Knowledge LevelThinking SkillOutcome
ProceduralAnalyseAbility to identify, prepare, and manage appropriate datasets for analysis.
ProceduralEvaluateAbility to analyse the results of data analyses, and to evaluate the performance of analytic techniques in context.
ProceduralApplyAbility to appropriately present the results of data analysis.
ProceduralEvaluateKnowledge and understanding of analytic techniques, and ability to appropriately apply them in context, making correct judgements about how this needs to be done.
 

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