Quantitative Risk Management with Machine Learning
MSc
Birkbeck, University of London
Key Course Facts
Course Description
In the wake of recent financial crises, major regulatory reforms have been imposed on the financial sector and this has led to a rise in demand for specialists in the field of financial risk management. The MSc in Quantitative Risk Management with Machine Learning has been designed to meet this demand. The course will provide you with the knowledge and the quantitative skills that are needed for a career in financial risk management. You will gain in-depth knowledge of financial derivatives, portfolio management and the core areas of risk management, with particular emphasis on market risk and credit risk.
Upon successful completion of this course, you will have the expertise to identify and manage the major sources of risk in the financial markets. You will understand the role of financial derivative products, their use (and misuse) and how they are priced, and you will be able to analyse financial data and build risk models, detect trends in data, test a given hypothesis and forecast future values. You will also understand how to build an investment portfolio (of risky assets) and carefully monitor its performance over time.
Birkbeck's postgraduate finance courses are built around our long-running experience in research-led evening teaching. Many of our students work in the finance industry, which generates a lively atmosphere in class and ensures that you'll be studying alongside committed, enthusiastic students with a wealth of experience. You will be taught by active researchers, working at the cutting edge in their respective fields, who are also experienced professional practitioners, giving specialist advice and in-house training to government departments and banks and firms in the City of London. This course is challenging and demanding, so you should demonstrate strong intellectual ability in your application and be willing to work hard.
Highlights
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This course combines a high level of academic rigour with a solid grounding in practical problem-solving skills.
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The emphasis is on machine learning, the branch of data science which is reshaping the finance industry. You will learn the statistical techniques and machine learning tools that are used widely in financial risk management and you will also learn how to programme Python and R.
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We have an excellent reputation for the quality of our teaching, which includes providing training for the Treasury and the Bank of England.
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Birkbeck's Department of Economics, Mathematics and Statistics enjoys an international reputation and its economics and finance research groups stand among the foremost research groups in the country.
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In the most recent Research Excellence Framework (REF), more than half of our research outputs in Economics were ranked world-leading or internationally excellent.
Entry Requirements / Admissions
Requirements for international students / English requirements
IELTS academic test score (similar tests may be accepted as well)
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- 6.5
- Mphil / PhD
Get advice on which foundation courses are best for you to still study Quantitative Risk Management with Machine Learning, MSc, if you do not meet the minimum requirements in terms of UCAS score, A levels, or English language requirements.
A second-class honours degree (2:2) or above in a quantitative subject, such as mathematics, physics, statistics, economics or engineering. Alternatively, a merit or higher in our MSc Finance. Applications are reviewed on their individual merits and your professional qualifications and/or relevant work experience will be taken into consideration positively. We actively support and encourage applications from mature learners.
Costs
Tuition Fees Quantitative Risk Management with Machine Learning MSc
| England UK | £13620 | year 1 |
|---|---|---|
| Northern Ireland | £13620 | year 1 |
| Scotland | £13620 | year 1 |
| Wales | £13620 | year 1 |
| International | £19830 | year 1 |
Additional fee information
Average student cost of living in London
| Rent | £518 |
| Water, gas electricity, internet (at home) | £50 |
| Supermarket shopping | £81 |
| Clothing | £35 |
| Eating out | £33 |
| Alcohol | £27 |
| Takeaways / food deliveries | £30 |
| Going out / entertainment (excl.alcohol, food) | £24 |
| Holidays and weekend trips | £78 |
| Transport within city | £17 |
| Self-care / sports | £20 |
| Stationary / books | £13 |
| Mobile phone / internet | £13 |
| Cable TV / streaming | £7 |
| Insurance | £51 |
| Other | £95 |
| Average student cost of living | £1092 |
London costs approx 34% more than average, mainly due to rent being 67% higher than average of other cities. For students staying in student halls, costs of water, gas, electricity, wifi are generally included in the rental. Students in smaller cities where accommodation is in walking/biking distance transport costs tend to be significantly smaller.
University Rankings
Positions of Birkbeck, University of London in top UK and global rankings.
See all 15 university rankings of Birkbeck, University of London
About Birkbeck, University of London
Birkbeck, University of London (BBK) was established in 1823, and takes a different stance towards admissions than many other universities. Applicants are encouraged by BKK to apply even if they don’t possess the traditional qualifications normally required, as applications here are also considered based on an assessment of your knowledge on the subject, and any work experience you might have. Being located in the very centre of London, students will have all the advantages of the city’s public transport connections, as well as the chance to live in such a diverse and vibrant city with all that it offers.
Student composition of Birkbeck, University of London
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Students by level of study Academic year 2020/21 - Full-time equivalent student enrollments published by Higher Education Statistics Agency (HESA) on 10 February 2022
- undergraduates:
- 5360
- postgraduates:
- 3160
- Total:
- 8520
List of 573 Bachelor and Master Courses from Birkbeck, University of London - Course Catalogue
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