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Machine Learning for Finance MSc (Online)

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 Accouting & Finance
Email:
Barry.Sheehan@ul.ie
Tel:
+353 61 232134
Name:
Kathy Ryan
Address:
Programme Co-ordinator, Taught Postgraduate Programmes
Email:
Kathy.Ryan@ul.ie
Tel:
+353-61-234389
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Read instructions on how to apply

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.   

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.

 

Year 1

Semester 1 (Autumn) Semester 2 (Spring) Summer
  • CE4021 Introduction to Scientific Computing for AI
  • FI6161 Capital Markets & Corporate Finance
 
  • CE6002 Artificial Intelligence and Machine Learning
  • CS5062 Data Analytics
 
  • IN5103 Risk, Ethics, Governance and Artificial Intelligence
  • FI6063 Advanced Topics Seminars and Project Specification

 

Year 2

Semester 1 (Autumn) Semester 2 (Spring) Summer
  • CS6134 Machine Learning Applications
  • FI6024 Machine Learning for Finance
  •  FI6035 Derivative Markets
  •  FI6015 Deep Learning for Finance

Electives (Choose Min 1, Max 1)

  • CS5004 Deep Learning
  • FI6025 Artificial Intelligence and Data Science Ecosystems: Theory and Practice
  • FI6016 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
  • English language qualification if English is not your first language
  • Certified English translations of your transcripts/certificates where the originals are in a language other than English.
  • A copy of your birth certificate/passport

English Language Requirements

Applicants whose first language is not English must provide evidence of either prior successful completion of a degree qualification taught through the medium of English or meet one of the criteria below (no longer than two years prior to application):

Acceptable English Language qualifications include the following:

  • Matriculation examinations from European countries where English is presented as a subject and an acceptable level is achieved
  • Irish Leaving Certificate English –Ordinary Level Grade D or above
  • TOEFL – 580 (paper based) or 90 (internet based)
  • IELTS – Minimum score of 6.5 with no less than 6 in any one component.
  • English Test for English and Academic Purposes (ETAPP) – Grade C1
  • GCE ‘O’ level English Language/GCSE English Language – Grade C or above
  • University of Cambridge ESOL –Certificate of Proficiency in English - Grade C / Certificate in Advanced English Grade A
  • GCE Examination Boards – Oxford Delegacy of Local Examinations – Grade C / Cambridge Local Examinations Syndicate – School Certificate Pass 1-6 / University of London Entrance and School Examinations Council – School Certificate Pass 1-6

Results in examinations other than those listed above may also be accepted as meeting our English language requirements. Contact the International Education Division for advice.

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