Machine Learning Technologies

Learning Outcomes

Subject Specific Practical Skills

Having successfully completed this module you will be able to:

• Gain facility in working with algorithms to handle data sets in a scientific computing environment
• Systematically work with data to learn new patterns or concepts

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• Characterise data in terms of explanatory models
• Gain a critical appreciation of Deep Learning

Knowledge and Understanding

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

• Implementation issues in learning algorithms and the evaluation of their performance.
• The algorithmic basis of machine learning algorithms

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.

Teaching and learning methods

Lectures, labs and guided self-study

Resources & Reading list

Textbooks

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

Summative

Summative assessment description

Referral

Referral assessment description

Repeat

Repeat assessment description

Repeat Information

Repeat type: Internal & External