Overview

Indicative content includes:

• Review of the fundamentals of machine learning: supervised and unsupervised learning, classifier models, feature selection.
• Classifier ensembles.
• Imbalanced data, streaming data and concept drift: machine learning solutions.
• Introduction to Deep Learning neural networks and their applications.

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 (by hand) some of the learned methods to toy problems.

• Compose thoughts and findings in a clear, scientific manner.

• Demonstrate knowledge of advanced machine learning methods at an algorithmic level.

• Program some of the learned methods to solve toy and real-world problems.

• Understand the challenges of modern data and the potential of machine learning to address them.