MSc Applied Machine Learning for Creatives

Course units

STEM for creatives

This unit offers a conversion boot camp for STEM study for arts and humanities graduates including the maths that underpins the data science approaches later in the course. This unit is taught by a STEM academics who have worked in a creative industries setting.

Natural language processing for the creative industries

This practical class develops key coding skills to support NLP for the creative industries and introduces applied computer science concepts for arts and humanities graduates. Students will use coding languages such Python and JavaScript to develop approaches to text analysis, text generation, chatbots, conversational interfaces for sectors such as data journalism, art practice, social media analysis and bias in large language models.

Introduction to data science

This computing and seminar class uses programming approaches to statistics, structuring data, analysing data and explores approaches to questioning real world datasets. This units also gives a grounding in data ethics, data handling and GDPR.

Artificial intelligence for media

This practical class introduces students to practical Artificial Intelligence tools such as Tensorflow an pyTorch in order to do signal processing classification, regression, style transfer, image and video generation and includes exploring techniques such as, deep fakes, GANS, pix-2-pix and others. You will benefit from tuition from senior CCI researchers in this area and our relationships with industrial product teams such as Google Brain.

Data science in the creative industries

This unit is taught in partnership with our current industry partner (WPP) and involves an industry case study of data science approaches to product development and applied approaches to campaign insight, customer interfaces, media analysis and generation.

Personalisation and machine learning

This practical class look at extending your machine learning experience to include the building and testing of recommenders and audience analysis tools. This Python and JavaScript based applied computing experience enables you to build and test systems that specifically test clustering for audience preferences.

Thesis project

This self-directed unit ask you to build a practical project and write an associated thesis report of 8-10,000 words that documents your technical methods, process of design and development and evaluation.

Learning and teaching methods

• Collaborative problem-solving and group work
• Independent study
• Lectures and seminars
• Project work
• Studio/lab-based practice and masterclasses
• Technical tuition

Selection criteria

Sufficient prior knowledge and experience of and/or potential in a specialist subject area to be able to successfully complete the programme of study and have an academic or professional background in a relevant subject for a conversion masters.

Aptitude for computing study and logical methods, sometimes tested in an online aptitude test. (this test is required on a per application basis and may not always be required)

Also, to show a willingness to work as a team player, good language skills in reading, writing and speaking, the ability to work independently and be self-motivated.

We welcome non-standard applications from diverse applicants and subject fields and applications that make a strong case for how the course could be applied to the ambitions of the applicant.