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During this programme, you will: 

• Discover how Artificial Intelligence and Machine Learning can transform processes and projects in financial services and unlock inspiring career opportunities. 
  
• Develop a powerful blend of business knowledge and applied skills. Draw on the expertise of our industry-led MSc in Artificial Intelligence, as well as the finance insight of Kemmy Business School. 
  
• Focus on practical skills and insights that you can immediately apply in the workplace.
• Benefit from lectures, tutorials, and assessments which are designed to be flexible, so you can learn in a way that works around you.

Key information: 

• Complete part-time over two years    

• Delivered online 

• Modules taught during autumn, spring and summer semesters    

• Submit dissertation at the end of the summer semester   

You will learn through a blend of:  

• Lectures, workshops, and hands-on activities  

• Reflective practice and guided research  

• Regular feedback from faculty and peers  

Part-time considerations:  

• Designed for working professionals  
   
• Evening or recorded lectures  
   
• Additional prep and group work time  
   
• Timetable provided after registration 

Year 1 

Autumn Semester 

• will introduce you to the relevant programming languages and host platforms for scientific computing. 

• will develop your knowledge of capital markets and corporate finance, including bond, derivative and foreign exchange markets. 

Spring Semester 

• introduces elements of data analytics workflow, including data clearing, predictive and descriptive modelling, and the deployment of models. 

• will build your awareness and understanding of derivative markets, including how to apply that knowledge to Machine Learning. 

Summer Semester 

• will highlight the importance of developing AI technologies alongside frameworks informed by risk, ethics and governance. 

• will begin the process of developing a research and development project, applying AI topics to a tangible business or societal need. 

Year 2 

Autumn Semester 

• will explain the role of the project manager, and help you understand how to deal with expectations and build and execute project plans. 

• will offer a practitioner-oriented education in the machine learning algorithms that are used in the capital markets and asset management sectors. 

Spring Semester 

• will develop your practical skills in deep learning algorithms, including an examination of recurrent neural networks (RNNs) and Long Short Term Memory Networks (LSTMs).
• will explore how to learn from data, build datasets, and develop algorithms which improve machine learning 

Summer Semester 

• module will see you research, develop, analyse and present a project on an AI or Machine Learning-related problem in finance. 

Books and journal articles needed for the course will be available online through the UL Glucksman Library. 

For more information on each module, you can search the faculty, school and module code on UL’s  

• Applicants should hold a bachelor’s degree ( Level 8) with at least a second-class honour, grade 2 (2:2) in the following disciplines: 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. 

Other Entry Considerations: 

We encourage you to apply even if you don’t meet the standard entry requirements, as long as you can show that you have the knowledge, skills, and experience needed for the programme. 

At UL, we value all kinds of learning and support different ways to qualify through our Recognition of Prior Learning (RPL) policy.  

International students:    

• For details on country-specific qualifications visit postgraduate entry requirements for international students. 

Checklist of documents:  

• *Academic transcripts and certificates
  • UL graduates only need to provide their student ID.
• Copy of your birth certificate or passport
• English translation of your qualifications and transcripts
• Personal statement - one page outlining your interest in the programme and explaining why your undergraduate education and/or work experiences meet the entry requirements of the programme. 

English Language: 

• English Language Competency certificate
• For details on accepted language qualifications visit English Language Requirements 

Guidelines on Completing your Application   

• To make sure we can review your application quickly, please:
  • Upload all documents. Your application can’t be reviewed until we have all the documents on the checklist. 
  • Title the documents you are uploading. For example, "Personal Statement", "Undergraduate Transcript", "Postgraduate Transcript", "English Language Certificate" etc. 
• *If you are waiting to graduate, submit your application with the documents you have to date, you don’t need to have finished final exams before applying 

EU - €8,800 per annum*

Non- EU - €10,600 per annum*

* Year 2 fees are subject to change 

Please note that international study visas are only available to students studying full-time in Ireland. Only the full-time version of this programme qualifies for a study visa.  

Annual fees are billed by semester. Once registered, students may be eligible to apply for a monthly payment plan.  

Further information on fees and payment of fees is available from the website. All fee related queries should be directed to the Student Fees Office (Phone: +353 61 213 007 or email student.fees.office@ul.ie).  

 
Funding 

Employment-sponsored applicants may receive co-funding by the ICBE Advanced Productivity Skillnet. This is a 20 per cent subsidy, however the level of funding and places available may change based on demand.   

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

Find further information on funding and scholarships.  

• Finance Managers
• Fund Managers
• Algorithmic Trading 

Still Curious?  

The team regularly host and take part in webinars to support future students. If you would like to learn more or ask questions at an online information session, click below.     

Online Information Sessions