Logo of Royal Holloway University of London

MScMachine Learning (Year in Industry)

More information

Expenses, accommodation, working etc.



Interested? To learn more about this study programme, entry requirements and application process, please contact one of our consultants in a country nearest to you.

Programme structure

Year 1
Data Analysis
This module covers algorithm-independent machine learning; unsupervised learning and clustering; exploratory data analysis; Bayesian methods; Bayes networks and causality; and applications, such as information retrieval and natural language processing. You will develop skills in data analysis, including data mining and statistics.

Computation with Data
In this module you will develop an understanding of the basics of algorithmic thinking and problem solving using programming. You will become familiar in using the Java programming language, examining particular features and constructs as well as basics of object-oriented programming. You will use these to solve specific algorithmic tasks and evaluate programming solutions.

Programming for Data Analysis
In this module you will learn how to use MATLAB (Matrix Laboratory) and WEKA (Waikato Environment for Knowledge Analysis) as tools for machine learning and data mining. For MATLAB, you will develop an understanding of how to input and output data using vectors, arrays and matrics; learn techniques in data visualization, including plots in 2 and 3 dimensions, scatter plots, barplots, and histograms; and learn how to implement concepts from linear algebra and statistics, including probability and matrix decompositions. For WEKA, you will develop an understanding of how to use the software as a tool for training and testing, predicting generalisation performance, and cross-validation; and learn how to implement decision trees, naïve Bayes classifiers, and clustering methods.

Machine Learning
In this module you will develop an understanding of modern machine learning techniques and gain practical experience in developing machine learning systems. You will look at the main advantages and limitations of the various approaches to machine learning and examine the features of specific machine-learning algorithms. You will also consider how the ideas and algorithms of machine learning can be applied in other fields, including medicine and industry.

On-line Machine Learning
In this module you will develop an understanding of the on-line framework of machine learning for issuing predictions or decisions in real-time. You will learn about protocols, methods and applications of on-line learning, covering probabilistic models based on Markov chains and their applications, such as PageRank and Markov Chain Monte-Carlo. You will examine the time series models, exploring their connections with Kalman filters, and learning models based on the prequential paradigm, including prediction with expert advice, aggregating algorithm, sleeping and switching experts. You will also consider universal algorithms, their application to portfolio theory, and how prediction within a confidence framework is achieved.

You will take either Inference or Applied Probability.

In this module you will develop an understanding of the mathematical theory underlying the main principles and methods of statistics, in particular, parametric estimation and hypotheses testing. You will learn how to formulate statistical problems in rigorous mathematical terms, and how to select and apply appropriate tools of mathematical statistics and advanced probability. You will construct mathematical proofs of some of the main theoretical results of mathematical statistics and consider the asymptotic theory of estimation.

Applied Probability
In this module you will develop an understanding of the principal methods of the theory of stochastic processes, and probabilistic methods used to model systems that exhibit random behaviour. You will look at methods of conditioning, conditional expectation, and how to generate functions, and examine the structure and concepts of discrete and continuous time Markov chains with countable state space. You will also examine the structure of diffusion processes.

Year 2
You will spend this year on a work placement. You will be supported by the Department of Computer Science and the Royal Holloway Careers and Employability Service to find a suitable placement. This year forms an integral part of the degree programme and you will be asked to complete assessed work. The mark for this work will count towards your final degree classification.

Individual Project
You will carry out an extended piece of individual work under the supervision of an academic member of staff, including the preparation of a dissertation and any programs you may have written. Your project may stress theoretical, methodological, or implementation aspects of a problem or case study, and you may wish to build on the experience that you will have gained during your placement.

Optional modules
In addition to these mandatory course units there are a number of optional course units available during your degree studies. The following is a selection of optional course units that are likely to be available. Please note that although the College will keep changes to a minimum, new units may be offered or existing units may be withdrawn, for example, in response to a change in staff. Applicants will be informed if any significant changes need to be made.

Career opportunities

A Masters in Machine Learning with a Year in Industry at Royal Holloway, University of London offers students excellent graduate employability prospects. You’ll develop practical skills in machine Learning Techniques, making you an attractive candidate to employers and gain invaluable skills, experience and connections during your year in industry. You’ll also be well-placed to pursue PhD study, should you choose to progress your studies further.

Our recent alumni have gone on to enjoy rewarding careers in a variety of computer science-related roles, including network systems design and engineering, web development and production. Our proximity to the M4 corridor technology hub – dubbed ‘England’s Silicon Valley’ – gives students the chance to enjoy excellent networking and placement opportunities with some of the country’s top technology organisations.

Apply now! Fall semester 2023/24
Application period has ended
Please see the university profile or contact us for the deadlines that apply to you
Apply now! Fall semester 2023/24
Application period has ended
Please see the university profile or contact us for the deadlines that apply to you