Introduction to Machine Learning

Learning Outcomes

Full CEng Programme Level Learning Outcomes

Having successfully completed this module you will be able to:

• As part of the exam, students must demonstrate their ability to apply the underlying mathematical principles from probability, linear algebra and optimisation to data analysis problems.
• As part of the coursework, students must demonstrate their ability to design and implement solutions for complex machine learning problems using a scientific computing environment.
• As part of the exam, students must demonstrate their ability to identify, analyse and evaluate sustainability issues that can arise from the use of data driven machine learning models.
• As part of the exam, students must demonstrate their ability to identify, analyse and evaluate ethics issues that can arise from the use of data driven machine learning models.
• As part of the coursework, students must demonstrate their ability to select and apply appropriate machine learning techniques to model data in terms of explanatory models and to systematically apply machine learning to data in order to learn new patterns or concepts.
• As part of the coursework and their exam, students must demonstrate their ability to appraise several state of the art machine learning methods, their implementation and limitations when applied to complex real world problems
• As part of the exam, students must demonstrate their ability to formulate, derive and analyse machine learning methods and technologies using the appropriate mathematical foundations.
• As part of the exam, students must demonstrate their ability to identify equality, diversity and inclusion issues that can arise from the use of data driven machine learning models.
• As part of the exam, students must demonstrate their ability to identify, analyse and evaluate security issues that can arise from the use of data driven machine learning models.

Knowledge and Understanding

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

• Formulate, derive and analyse machine learning methods and technologies using the appropriate mathematical foundations.
• Apply the underlying mathematical principles from probability, linear algebra and optimisation to solve data analysis problems.

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• Select and apply appropriate machine learning techniques to describe data in terms of explanatory models.
• Design and implement solutions for complex machine learning problems using a scientific computing environment.
• Systematically apply machine learning to data in order to learn new patterns or concepts.

Subject Specific Practical Skills

Having successfully completed this module you will be able to:

• Critically appraise several state-of-the-art machine learning methods, their implementation and limitations when applied to complex real-world problems.

Mathematical and statistical foundations

• Linear algebra and vector spaces
• Cost functions, optimisation and stochastic gradient descend
• Discrete and continuous random variables
• Distributions and conditional distributions
• Hypothesis testing, likelihood optimisation and Bayesian methods

Machine Learning basics:

• Supervised vs. unsupervised learning
• Classification, regression, clustering and dimensionality reduction
• Performance measures and empirical risk minimization
• Overfitting, training, testing, validating
• Bias variance trade-offs
• Classical machine learning methods and artificial neural networks

Modern deep learning models:

• Deep feedforward and recurrent models
• Auto-encoders
• Generative models

Application examples:

• Multivariate data
• Image data
• Time series data

Common scientific computing tools for machine learning:

• Data handling tools
• Machine learning tools
• Visualisation tools

Societal issues:

• Data biases in machine learning and their impact on equality, diversity and inclusion
• Machine learning ethics
• Sustainability of machine learning solutions
• Security of machine learning systems

Lectures, tutorials, computing labs, data analysis and computing exercises, and guided self-study

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.