Course units
Term 1
Advanced Algorithms and Complexity (20 credits)
You will be introduced to advanced algorithms through mathematics and programming, including linear algebra for advanced analysis of data and machine learning optimisation. You will create and analyse computational models using approaches such as stochastic and gradient algorithms, dynamic programming algorithms and primal and dual methods. This will develop your understanding of how algorithms might be improved to tackle current and emerging problems.
Advanced Mathematics and Statistics for Data Science and AI (20 credits)
You will learn advanced data structures and representations, including complex multidimensional feature processing and storage. You will be asked to demonstrate your advanced knowledge and skills in a range of mathematical and statistical approaches required for carrying out modern data science and AI. This includes calculus, discrete structures, probability theory and elementary statistics. You will also approach advanced topics in statistics including complex correlations, significance, differences in nominal and ordinal data analysis, and linear algebra.
Term 2
Critical Data Representation and Analysis (20 credits)
This unit will cover advanced professional practice principles, ethics, data protection legislation, compliance procedures and impact analysis. Through a series of case studies, you will be introduced to different critical approaches, such as social data science and you will explore in detail how representation and data abstraction at macro scale can impact individuals and marginalised groups. This would be a foundation for the unit Data, People and Society. You will also learn how to story tell through various data visualization techniques and get introduced to big data tools and techniques.
Artificial Intelligence and Machine Learning (20 credits)
This unit focuses on a range of contemporary AI and machine learning techniques and approaches such as RNN and LSTMs, GANs and VAEs. You will also cover reinforcement learning for natural language processing, personalisation, recommendation and audience analysis. As part of this unit, you will learn how to prepare datasets and create, test and validate your own models to solve real-world problems.
Term 3
Data, People and Society: Advanced Topics (20 credits)
In this unit, you will continue to develop your understanding of computing ethics, social data science and international data policy. You will learn how to analyse and apply critical approaches to technology development within your own work. You will be expected to apply this knowledge in your thesis project, exploring how you have embedded computing ethics and techniques in your own approach to work.
Computational Entrepreneurship and Ethics (20 credits)
This MSc course has a strong focus on ‘tech for good’ and seeks to contribute to UAL’s social purpose mission. In this unit, you will develop your skills in entrepreneurship and futures thinking and learn how to embed ethical computing into your own practice. You will be introduced to a range of product development case studies, evaluating their social, cultural and ethical impact. This contextual knowledge will help you to develop realistic, informed project plans, considering team requirements, investment requirements and market placement.
Summer period
Thesis Project (60 credits)
Your final thesis project will allow you to develop a significant piece of work demonstrating the level of your knowledge and skills in relation to those delivered throughout the course. Academic staff will support you throughout your project, sharing their professional experience in contemporary data science and AI. You will also be offered the opportunity to work with staff to develop research projects based on staff expertise and topic specialisms as an option.
