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Machine Learning is the study of making accurate, computationally efficient, interpretable and robust inferences from data, often drawing on principles from statistics. This subject aims to introduce students to the intellectual foundations of machine learning, including the mathematical principles of learning from data, algorithms and data structures for machine learning, and practical skills of data analysis.
Indicative content includes: cleaning and normalising data, supervised learning (classification, regression, linear & non-linear models), and unsupervised learning (clustering), and mathematical foundations for a career in machine learning.
Intended learning outcomes
On completion of this subject the student is expected to:
- Understand elementary mathematical concepts used in machine learning
- Derive machine learning models from first principles
- Design, implement, and evaluate machine learning systems for real-world problems
- Identify the correct machine learning model for a given real-world problem
- On completion of this subject, students should have the following generic skills:
- General skills include the ability to undertake problem identification, formulation, and developing solutions especially exploiting acquired data
- In addition this subject exposes students to use various data processing tools and make them learn integration of these tools to build more complex software systems
- As a result the student will develop skills to utilise a systems approach to complex problems
Last updated: 29 April 2020