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This module introduces foundational concepts and algorithms of machine learning. The module will cover general concepts, such as regression, classification, density estimation, and dimensionality reduction as well as techniques for computing intractable integrals. The module consists of lectures, laboratory work (coding-based tutorials). Algorithms will be connected to real-world data problems, so that learners can complete research-like tasks, drawing on a range of sources, with a significant level of autonomy. Learners will gain an understanding of the material and main concepts/theories taught in this module. They will develop skills for analysis and synthesis. Knowledge of research-informed literature will be an outcome of this module. Learners will be able to identify key areas of problems and choose appropriate methods for their resolution.

Aims:

The aims of this module are to:

1. Provide students with a strong foundational understanding of machine learning, particularly for follow-up modules, such as deep learning, reinforcement learning, and robot learning.
2. Support students in the development of a breadth of knowledge and understanding in the fundamentals of regression, classification, density estimation, dimensionality reduction, and model selection with the goal of applying this to data-modelling problems.
3. Provide an applied context for the use of fundamental concepts in object-oriented programming in the creation of programs for machine learning applications.

Intended learning outcomes:

On successful completion of this module, a student will be able to:

1. Develop a systematic approach to analyzing data using machine learning.
2. Evaluate the quality and suitability of different machine learning methods for modelling data.
3. Examine properties of machine learning algorithms using data interpretation.
4. Develop and build on basic elements of the programming paradigm and the ability to compose these to produce programs that function as intended, scale efficiently in a multi-processor environment, and deliver machine learning results.

Indicative content:

The following are indicative of the topics the module will typically cover:

• Regression (linear regression).
• Classification (logistic regression, SVMs).
• Naïve Bayes.
• Density estimation (GMMs).
• Clustering.
• Linear dimensionality reduction (PCA).
• Decision trees.
• Model selection (cross validation, error analysis).
• Neural networks.
• ML ethics and fairness.

Requisite conditions:

To be eligible to select this module as optional or elective, a student must be registered on a programme and year of study for which it is formally available.