Overview

Machine learning lies at the crossroads of statistics and computer science, permeating diverse sectors including science, high-tech, retail, finance, transportation, and more. At its core, machine learning strives to craft data-powered models for comprehending and foreseeing real-world system behaviors, fueling the heightened demand for machine learning expertise.

This module serves as an introduction to the fundamentals of machine learning. Guided by a practical approach (and less on the mathematical details), you will delve into key concepts, methodologies, and tools essential for crafting and assessing machine learning solutions. By engaging in hands-on experiences, you will gain a robust grasp of the intricacies of machine learning. Equipped with this foundation, you'll be empowered to independently advance your machine learning prowess, all the while adeptly scrutinising forthcoming developments in the expansive realm of data science.

Indicative content includes: - Explain and apply the fundamental notions and principles of machine learning. - Detail and apply various classification models. - Detail and apply clustering algorithms to data sets. - Explain dimensionality reduction, its approaches and methods. - Introduction to neural network models and their training procedures.

Assessment Strategy

• Threshold: Equivalent to 50%. Uses key areas of theory or knowledge to meet the Learning Outcomes of the module. Is able to formulate an appropriate solution to accurately solve tasks and questions. Can identify individual aspects, but lacks an awareness of links between them and the wider contexts. Outputs can be understood, but lack structure and/or coherence.

• Good: Equivalent to the range 60%-69%. Is able to analyse a task or problem to decide which aspects of theory and knowledge to apply. Solutions are of a workable quality, demonstrating understanding of underlying principles. Major themes can be linked appropriately but may not be able to extend this to individual aspects. Outputs are readily understood, with an appropriate structure but may lack sophistication.

• Excellent: Equivalent to the range 70%+. Assemble critically evaluated, relevant areas of knowledge and theory to construct professional-level solutions to tasks and questions presented. Is able to cross-link themes and aspects to draw considered conclusions. Presents outputs in a cohesive, accurate, and efficient manner.

Learning Outcomes

• Apply the machine learning taxonomy to formulate meaningful questions and identify appropriate techniques to address them.

• Apply the methodology needed to build and evaluate machine learning solutions.

• Detail and apply clustering algorithms to data sets.

• Detail and apply various classification models.

• Explain and apply the basic notions and principles of machine learning.

• Explain dimensionality reduction, its approaches and methods.

• Summarise neural network models and their training procedures.