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During this programme, you will:  

• Explore a wide range of AI techniques, including reinforcement learning, deep learning, evolutionary computation, Bayesian networks and search algorithms. 

• Examine emerging disciplines and technologies, such as deep reinforcement learning, causality modelling, bioinspired robotics, evolutionary robotics, games-inspired behaviour modelling, probabilistic programming and explainable AI. 

• Apply AI techniques to a variety of application areas such as text analytics and natural language processing, machine vision, games and smart manufacturing. 

• Acquire the skills to research, analyse and present a project based on a chosen topic or problem. 

Key information: 

• Complete full-time over one year 

• Delivered on campus  

• Modules taught during autumn and spring semesters  

• Submit dissertation or Learning Project at the end of 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 

Year 1  

Autumn Semester 

• covers mathematical and coding skills essential to developing machine learning applications in Python and introduces more advanced machine learning topics. 

• introduces Evolutionary Algorithms and delves into specific algorithms, including Genetic Programming, Grammatical Evolution and more. 

• provides a practical knowledge of text analysis techniques and Natural Language Processing. 

• provides a foundation in the principles and applications of Artificial Intelligence methods as applied to Games and Game Development. 

•  the aim of this module is to allow the student to gain a detailed insight into image formation, formats and processing necessary so computers can use machine vision technologies. 

Spring Semester 

• the module covers what it is we mean by learning from data and using patterns in data to learn from it as long as we have enough data from which to learn. We will look at how to learn from data, using basic techniques and trialling them on data sets, we will look a algorithms to make or learning better. 

• provides fundamental knowledge and techniques necessary to understand/develop/use Deep Reinforcement Learning. 

• introduces probabilistic programming and defines probabilistic AI models and inference algorithms. 

• prepares students to understand and use a wealth of new technologies relevant to the production and use of Artificial Intelligence. 

• helps students to specify a project and demonstrate research skills. 

Summer Semester 

• enables students to research, analyse and present a project based on a chosen topic or problem. 

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 honours, grade 2 (2:2) in a relevant or appropriate subject like computer science, computer engineering programmes, or science programmes that have sufficient mathematical and programming skills. 

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 

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 - €8,200

Non-EU - €20,800

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 Student Fees Office 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 

Find further information on funding and scholarships.

This course can lead to the following sectors and careers:  

• Data analytics
• Data science
• Machine learning
• AI

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