Course Details

Available:
Part-Time
Duration:
2 Years Part Time - Fully Online
Award:
Postgraduate
Qualification:
NFQ Level 9 Major Award
Faculty: Kemmy Business School
Course Type: Taught, Professional/Flexible
Fees: For Information on Fees, see section below.

Contact(s):

Name: Dr Barry Sheehan
Address: Dept. of Accounting & Finance Email: Barry.Sheehan@ul.ie Telephone: +353 61 232134
Name: Riah Hogan
Address: Programme Co-ordinator, Taught Postgraduate Programmes Email: Riah.Hogan@ul.ie

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Brief Description

The MSc in Machine Learning for Finance is a unique, interdisciplinary programme which blends applied, practical financial theory with an advanced technical skillset derived from computer science. The MSc in Machine Learning for Finance is the first, fully online programme of its kind in Ireland.

Employment-sponsored applicants may receive co-funding by the ICBE Advanced Productivity Skillnet.   For further information please click here

The MSc in Machine Learning for Finance is a unique, interdisciplinary programme which blends applied, practical financial theory with an advanced technical skillset derived from computer science. 
The MSc in Machine Learning for Finance is the first, fully online programme of its kind in Ireland. 
The programme leverages the experience, knowledge and expertise from the industry-led MSc in Artificial Intelligence. Combined with the award-winning finance postgraduate offerings in the Kemmy Business School, this programme provides a focussed upskilling initiative addressing widening AI skills shortage in the financial services industry.
With the financial services industry posited to become mass adopters of AI in the near future; this programme is aimed at those working in financial services, professional services and data analysis roles who wish to upskill and reskill to meet the considerable industry demand for applied technical skills combined with strong business acumen.

Programme delivery
 
The programme is delivered primarily via recorded online lectures, supported with tutorials, assignments and live webinars. Assessment is based mainly on assignments and project work with a practical rather than theoretical focus. The focus is on asynchronous (flexible) delivery and assessment. All relevant course material (books, journal articles, etc.) required will be available digitally via the UL Glucksman Library’s online resources.
 
Modules will be delivered with an associated assessment of mastery so that semester by semester there is a confirmed and measurable achievement of learning objectives that can be transferred directly and immediately to the workplace.

There is 90 ECTs for the programme.

Athena Swan Scholarships available - to apply please see Faculty Scholarships (KBS) on our Funding your Postgraduate degree page. Closing date: Friday 20th May 2022. - CLOSED

Year 1

Semester 1 (Autumn)

Semester 2 (Spring)

Summer

  • CE4021 Introduction to Scientific Computing for AI
  • FI6161 Capital Markets & Corporate Finance
  • CS5062 Data Analytics
  • FI6035 Derivative Markets
  • IN5103 Risk, Ethics, Governance and Artificial Intelligence
  • CS6163 Advanced Topics Seminars and Project Specification

Year 2

Semester 1 (Autumn)

Semester 2 (Spring)

Summer

  • MN6041 Project Management in Practice
  • FI6024 Machine Learning for Finance

 

  •  FI6015 Deep Learning for Finance
  • CE6002 Artificial Intelligence and Machine Learning

 

  • FI6026 Project and Dissertation – Machine Learning for Finance

Content of modules can be found by using the search option on the book of modules.


 

Applicants must hold a Level 8 honours degree at a minimum second class honours, grade 2 (NQF or other internationally recognised equivalent) in a relevant  discipline such as finance, economics, business, engineering, computing, mathematics, science or technology.

Applicants from other disciplines who have relevant mathematics and computing elements in their primary degree will also be considered.

Applicants who possess an honours degree, minimum 2nd class, grade 2, or equivalent in a non-numerate discipline and have three years experiential learning in an appropriate computing discipline will be considered.

RPL (Recognised Prior Learning) entry will be available for those who do not meeting the minimum entry requirement but who have gained substantial experience in the area.

WHAT TO INCLUDE WITH YOUR APPLICATION:

  • Qualification transcripts and certificates
  • A copy of your birth certificate/passport
  • If your qualifications have been obtained in a country where English is an official language this will suffice
  • If this is not available, the following additional documents must be provided:
    • English translation of your qualification(s)/transcripts
    AND
    • English language competency certificate

    For more information Click Here

For fees please click here.

Note: Only employment-sponsored applicants may receive co-funding by the ICBE Advanced Productivity Skillnet. The funding level is 20% subsidy on the cost but may change depending on demand. Depending on demand, we may need to limit the number of places we can fund and/or the number of employees per company. 

For information regarding ICBE funding only, please contact :
Aidan Kelly | Project & Network Manager 
Email: aidan@icbe.ie 

Please click here for information on funding and scholarships.