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COMP6246 Machine Learning Technologies (MSc)

Module Overview

Machine Learning is about extracting useful information from large and complex datasets. The module will cover the practical basis of how learning algorithms are can be applied. You will gain hands-on experience in laboratory-bases sessions. Exclusions: Cannot be taken with COMP3206 or COMP3222 or COMP3223 or COMP6229 or COMP6245.

Aims and Objectives

Module Aims

To give students a solid grounding in the concepts of machine learning using hands-on practical work and algorithmic-level understanding.

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

  • The algorithmic basis of machine learning algorithms
  • Implementation issues in learning algorithms and the evaluation of their performance.
Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

  • Characterise data in terms of explanatory models
  • Critically appraise the uses of Deep Learning
  • Appreciation of the landscape of tools used for modern machine learning
Subject Specific Practical Skills

Having successfully completed this module you will be able to:

  • Systematically work with data to learn new patterns or concepts
  • Gain expertise in working with machine learning to make predictions in a scientific computing environment

Syllabus

Historical Perspective - Biological motivations: the McCulloch and Pitts neuron, Hebbian learning. - Conceptual motivations Tools in machine learning - Libraries - Implementing and evaluating algorithms Supervised Learning - Perceptron Algorithm - Support Vector Classification and Regression - Neural networks/multi-layer perceptrons (MLP) Data handling and unsupervised learning - Principal Components Analysis (PCA) - K-Means clustering Regression and Model-fitting Techniques - Linear regression Deep Learning - Deep Neural Networks (CNN, RNN) Case Studies - Example applications: Speech, Vision, Natural Language, Bioinformatics.

Learning and Teaching

Teaching and learning methods

Lectures, labs and guided self-study

TypeHours
Lecture24
Specialist Laboratory20
Revision10
Wider reading or practice21
Completion of assessment task75
Total study time150

Resources & Reading list

Aurélien Géron. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. 

Assessment

Summative

MethodPercentage contribution
Coursework 50%
Examination  (2 hours) 50%

Repeat

MethodPercentage contribution
Examination 100%

Referral

MethodPercentage contribution
Examination  (2 hours) 100%

Repeat Information

Repeat type: Internal & External

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