Mathematics with Machine Learning

This is a Connected Degree
Portsmouth is the only University in the UK with the flexibility to choose when to do an optional paid placement or self-employed year. Either take a placement in your third year, or finish your studies first and complete a placement in your fourth year. You can decide if and when to take a placement after you've started your course.

Overview
Understand the mathematics that underpins artificial intelligence, and develop the skills needed to build machine learning models.

You’ll make yourself vital to an age of artificial intelligence by building invaluable theoretical and practical abilities. You’ll study powerful mathematical concepts and tools, and bring them to bear on subjects like machine learning, neural networks, and Python coding.

Once you graduate, you’ll be set to enter any of the industries being transformed by AI and machine learning tools. You'll learn how to apply large language models such as ChatGPT, and how to analyse images and other live data coming from sectors such as healthcare, education and business. You'll also be ready to move into roles that rely on mathematical understanding, such as finance or government, or to take up postgraduate study in maths or artificial intelligence.

Course highlights

• Develop a rounded understanding of modern mathematics, including calculus, linear algebra and probability, with a focus on machine learning tools, theories and methods

• Apply your learning with modules in programming languages such as Python, Mathematica and R

• Learn how to use industry standard tools for building machine learning models such as scikit-learn, PyTorch and TensorFlow

• Study alongside world-class researchers in machine learning and mathematics, in a department placed in the top ten for teaching in the 2022 NSS report

• Build your career prospects with built-in employability programmes, placement support and careers advice

• Brush up your skills with our drop-in Maths Cafe and personal tutorial system

Careers
Studying machine learning shows you’re committed to understanding the needs of the growing artificial intelligence sector. Forbes magazine predicts a 71% growth in jobs that need AI or machine learning skills by 2026, and research suggests that the UK will face a skill gap that your knowledge could help fill.

You’ll also graduate with a deep understanding of the mathematical principles, theories and methods that make machine learning possible - unlike other degrees in this field, our degree in Mathematics and Machine Learning is designed to give you the underlying understanding that will help you grasp future developments in the sector.

Additionally, your mathematical study will make you employable in sectors beyond machine learning, as you’ll be able to show your readiness for careers in finance, analysis, or anywhere that analytical problem-solving is a bonus.

Typical roles

You can expect to apply for roles like “machine learning engineer” or “machine learning scientist”; or, more broadly, titles like “data engineer” or “data scientist”. More generally, you’ll find your ability to build models that learn from data is in demand in sectors such as finance, education, retail, defence, government research.

Year 1

• Calculus I (20 credits)
• Computational Mathematics (20 credits)
• Linear Algebra (20 credits)
• Mathematical Foundations (20 credits)
• Mathematical Models (20 credits)
• Statistical Theory and Methods I (20 credits)

There are no optional modules in Year 1

Year 2
Core modules include:

• Applications of Mathematics and Graduate Skills (20 credits)
• Calculus II (20 credits)
• Mathematical Methods for Machine Learning (20 credits)

Optional modules include:

• Algebraic Structures & Discrete Mathematics (20 credits)
• Exchange Study Abroad - School of Mathematics and Physics (40 credits)
• Mathematics for Finance (20 credits)
• Mechanics and Dynamics (20 credits)
• Operational Research (20 credits)
• Real and Complex Analysis (20 credits)
• Statistical Theory & Methods Ii (20 credits)
• Universe: Planetary Systems, Stars and Galaxies (20 credits)

Year 3

Core modules include:

• Advanced Machine Learning (20 credits)
• Statistical Learning (20 credits)

Optional modules include:

• Advanced Decision Modelling (20 credits)
• Financial Derivative Pricing (20 credits)
• Introduction to General Relativity and Cosmology (20 credits)
• Modern Astrophysics 1 (20 credits)
• Nonlinear Dynamics (20 credits)
• Partial Differential Equations and Their Applications (20 credits)
• Project (20 credits)
• Quantitative Supply Chain Management (20 credits)
• Statistics Methods in Health Research & Social Science (20 credits)
• Undergraduate Ambassador (20 credits)

Placement year

After your second or third year, you can do an optional study abroad or work placement year to get valuable longer-term work experience in the industry. We’ll help you secure a work placement that fits your aspirations. You’ll get mentoring and support throughout the year.

Changes to course content

We use the best and most current research and professional practice alongside feedback from our students to make sure course content is relevant to your future career or further studies.

Therefore, some course content may change over time to reflect changes in the discipline or industry. If a module doesn't run, we'll let you know as soon as possible and help you choose an alternative module.

You'll be assessed through written and practical exams, coursework and in-class tests. While most modules have an exam element, no module is wholly based on a single exam result.

You’ll be able to test your skills and knowledge informally before you do assessments that count towards your final mark, and use feedback from your practice and formal assessments so you can improve in the future.