Mathematics with Machine Learning
University of Portsmouth
UCAS Code: G500 | Bachelor of Science (with Honours) - BSc (Hons)
University of Portsmouth
UCAS Code: G500 | Bachelor of Science (with Honours) - BSc (Hons)
Entry requirements
A level
112-120 points from 2 or 3 A levels, including 40 points from Mathematics.
112-122 Tariff points from the Access to HE Diploma (Mathematics based).
Cambridge Pre-U score of 54-56, to include a Principal Subject in Mathematics at M2.
GCSE/National 4/National 5
GCSE English and mathematics at grade C/4 or above.
International Baccalaureate Diploma Programme
27 points from the IB Diploma with 555 at Higher Level, with 5 points from a Higher Level in mathematics.
Leaving Certificate - Higher Level (Ireland) (first awarded in 2017)
H3,H3,H3,H3,H4-H3,H3,H3,H3,H3
To include Higher Level Mathematics at H3.
Acceptable when combined with other qualifications.
Acceptable when combined with other qualifications.
112-120 points to include 40 points from A level Mathematics.
112-120 Tariff points to include 40 points from Advanced Level Mathematics.
UCAS Tariff
112-120 points from 2 or 3 A levels, or equivalent, including 40 points from Mathematics.
112-120 points from the Advanced Welsh Baccalaureate including 2 A levels one of which must be Mathematics at grade B, plus the Advanced Skills Challenge Certificate.
About this course
This course has alternative study modes. Contact the university to find out how the information below might vary.
**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.
Modules
Year 1
Calculus I (20 credits)
Introduction to Computational Methods (20 credits)
Linear Algebra (20 credits)
Mathematical Foundations (20 credits)
Mathematical Models (20 credits)
Statistical Theory and Methods I (20 credits)
Year 2:
Core modules:
Applications of Mathematics and Graduate Skills (20 credits)
Calculus II (20 credits)
Mathematical Methods for Machine Learning (20 credits)
Optional modules:
Algebraic Structures and Discrete Mathematics (20 credits)
Introduction to Astrophysics and Cosmology (20 credits)
Mathematics for Finance (20 credits)
Mechanics and Dynamics (20 credits)
Operational Research (20 credits)
Real and Complex Analysis (20 credits)
Statistical Theory and Methods II (20 credits)
Year 3:
Core modules:
Advanced Machine Learning (20 credits)
Statistical Learning (20 credits)
Optional modules:
Undergraduate Ambassador (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)
Quantitative Supply Chain Management (20 credits)
Statistics Methods in Health Research and Social Science (20 credits)
Project (20 credits)
Advanced Decision Modelling (20 credits)
Placement year (Optional)
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.
Assessment methods
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.
The Uni
University of Portsmouth
Faculty of Technology
What students say
We've crunched the numbers to see if the overall teaching satisfaction score here is high, medium or low compared to students studying this subject(s) at other universities.
How do students rate their degree experience?
The stats below relate to the general subject area/s at this university, not this specific course. We show this where there isn’t enough data about the course, or where this is the most detailed info available to us.
Mathematics
Teaching and learning
Assessment and feedback
Resources and organisation
Student voice
Who studies this subject and how do they get on?
Most popular A-Levels studied (and grade achieved)
Statistics
Sorry, no information to show
This is usually because there were too few respondents in the data we receive to be able to provide results about the subject at this university.
Who studies this subject and how do they get on?
Most popular A-Levels studied (and grade achieved)
Artificial intelligence
Sorry, no information to show
This is usually because there were too few respondents in the data we receive to be able to provide results about the subject at this university.
Who studies this subject and how do they get on?
Most popular A-Levels studied (and grade achieved)
After graduation
The stats in this section relate to the general subject area/s at this university – not this specific course. We show this where there isn't enough data about the course, or where this is the most detailed info available to us.
Mathematics
What are graduates doing after six months?
This is what graduates told us they were doing (and earning), shortly after completing their course. We've crunched the numbers to show you if these immediate prospects are high, medium or low, compared to those studying this subject/s at other universities.
Top job areas of graduates
Want to feel needed? This is one of the most flexible degrees of all and with so much of modern work being based on data, there are options everywhere for maths graduates. With all that training in handling figures, it's hardly surprising that a lot of maths graduates go into well-paid jobs in the IT or finance industries, and last year, a maths graduate in London could expect a very respectable average starting salary of £27k. And we're always short of teachers in maths, so that is an excellent option for anyone wanting to help the next generation. And if you want a research job, you'll want a doctorate — and a really good maths doctorate will get you all sorts of interest from academia and finance — and might secure some of the highest salaries going for new leavers from university.
Statistics
What are graduates doing after six months?
This is what graduates told us they were doing (and earning), shortly after completing their course. We've crunched the numbers to show you if these immediate prospects are high, medium or low, compared to those studying this subject/s at other universities.
Top job areas of graduates
The business and research sectors worry that the UK hasn't got enough people with good statistics skills, and as stats are at the heart of so much of the economy, and we only have a few hundred graduates a year in the discipline, this type of degree can be very useful and versatile. The finance industry is very popular with this group, and they're far more likely to be working in London than most other graduates. And who can blame them — statistics graduates starting work in London were earning an average of nearly £29k just six months after leaving university. There is also demand from the Scottish finance sector in Edinburgh and Glasgow - particularly in banking and insurance. But a good statistician can find work almost anywhere that data can be analysed - which, in an online world, is almost anywhere - and many industries struggle to find enough statisticians to fulfil demand, so stay flexible and you can find a variety of options.
Artificial intelligence
What are graduates doing after six months?
This is what graduates told us they were doing (and earning), shortly after completing their course. We've crunched the numbers to show you if these immediate prospects are high, medium or low, compared to those studying this subject/s at other universities.
Top job areas of graduates
What about your long term prospects?
Looking further ahead, below is a rough guide for what graduates went on to earn.
Mathematics
The graph shows median earnings of graduates who achieved a degree in this subject area one, three and five years after graduating from here.
£22k
£27k
£35k
Note: this data only looks at employees (and not those who are self-employed or also studying) and covers a broad sample of graduates and the various paths they've taken, which might not always be a direct result of their degree.
Statistics
The graph shows median earnings of graduates who achieved a degree in this subject area one, three and five years after graduating from here.
£22k
£27k
£35k
Note: this data only looks at employees (and not those who are self-employed or also studying) and covers a broad sample of graduates and the various paths they've taken, which might not always be a direct result of their degree.
Artificial intelligence
The graph shows median earnings of graduates who achieved a degree in this subject area one, three and five years after graduating from here.
£23k
£29k
£31k
Note: this data only looks at employees (and not those who are self-employed or also studying) and covers a broad sample of graduates and the various paths they've taken, which might not always be a direct result of their degree.
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This information comes from the National Student Survey, an annual student survey of final-year students. You can use this to see how satisfied students studying this subject area at this university, are (not the individual course).
This is the percentage of final-year students at this university who were "definitely" or "mostly" satisfied with their course. We've analysed this figure against other universities so you can see whether this is high, medium or low.
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This information is from the Higher Education Statistics Agency (HESA), for undergraduate students only.
You can use this to get an idea of who you might share a lecture with and how they progressed in this subject, here. It's also worth comparing typical A-level subjects and grades students achieved with the current course entry requirements; similarities or differences here could indicate how flexible (or not) a university might be.
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Post-six month graduation stats:
This is from the Destinations of Leavers from Higher Education Survey, based on responses from graduates who studied the same subject area here.
It offers a snapshot of what grads went on to do six months later, what they were earning on average, and whether they felt their degree helped them obtain a 'graduate role'. We calculate a mean rating to indicate if this is high, medium or low compared to other universities.
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Graduate field commentary:
The Higher Education Careers Services Unit have provided some further context for all graduates in this subject area, including details that numbers alone might not show
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The Longitudinal Educational Outcomes dataset combines HRMC earnings data with student records from the Higher Education Statistics Agency.
While there are lots of factors at play when it comes to your future earnings, use this as a rough timeline of what graduates in this subject area were earning on average one, three and five years later. Can you see a steady increase in salary, or did grads need some experience under their belt before seeing a nice bump up in their pay packet?
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