Machine Learning for Finance
Unique, flexible, online programme at the cutting edge of quantitative finance and computer science.
• Introduction to Scientific Computing for AI
• Capital Markets & Corporate Finance
• Derivative Markets
• Data Analytics
• Risk, Ethics, Governance and Artificial Intelligence
• Advanced Topics Seminars & Project Specification
• Project Management in Practice
• Machine Learning for Finance
• Artificial Intelligence and Machine Learning
• Deep Learning for Finance
• Project & Dissertation: Machine Learning for Finance
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.
How to apply:
1. Choose your programme.
2. Check closing date for the programme.
3. Apply online at www.ul.ie
4. Have your supporting documentation ready to upload.
5. Pay the application fee (€35 online / €40 bank draft or cheque).
6. Submit your application.
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
• English language competency certificate
2 Years Part-Time. Online.
The programme is delivered primarily via recorded online lectures, supported with tutorials, assignments and live webinars. All relevant course material will be available digitally via the UL Glucksman Library online resources.