Those with knowledge of basic machine learning and statistical methods for data analysis, but who wish to progress to more sophisticated machine learning and AI based methods. You are likely in a data science role but would like to acquire the skills required to build your own deep-learning algorithms, or related methods.
Machine Learning and AI
Overview
| Module Code | STAT40970 |
| Module Title | Machine Learning and AI |
| Subject Area | Data Science |
| Credits | 5 |
| NFQ | 9 |
| EFQ | 7 |
| Start Date | 19th January 2026 |
| Duration | 12 Weeks |
| Time | N/A |
| Mode of Delivery | Online |
| Course Leader | Dr. Michael Fop |
| Fee | €900 |
| Application Deadline | 5th January 2026 |
Machine learning makes predictions from data with a focus on algorithmic efficiency and optimization with respect to prediction accuracy. Following on from the Statistical Machine Learning module, this module explores crucial topics in machine learning within the context of artificial intelligence, including neural networks, deep learning, big data applications, benchmarking of prediction methods. The overarching objective is to demonstrate the utilization of algorithms capable of learning and making predictions from complex data, including self-tuning and adaptation to diverse data structures. While these methods often pose challenges in interpretation and inherently exhibit a black-box nature, the module explores important considerations in their construction, use, interpretation, and comparison. Implementation of these machine learning methods is covered using the statistical software R and the Python Keras library, providing a hands-on approach to reinforce theoretical concepts.
On completion of this module, students should have acquired the following skills:
- Have an understanding of the theory regarding all the machine learning and artificial intelligence methods introduced.
- Being able to apply a range of machine learning and artificial intelligence methods, including deep learning.
- Being able to tune and evaluate the performance of the methods introduced, benchmarking them against each other based on out-of-sample prediction performance.
- Use the statistical software R and the Python Keras library to implement these methods.
Indicative module content:
- Introduction to machine learning and artificial intelligence
- Data pre-processing and big data
- Foundations of machine learning
- Model training, tuning, and evaluation
- Neural networks
- Deep learning and deep neural networks
- Convolutional neural networks - Recurrent neural networks
You will become expert in building, tuning, and critiquing complex machine learning and AI based algorithms for analysing complex datasets.
Video lectures posted each week that walk through module content, blending theory with example exercises. Practice problem sheets to enable self-assessment of learning outcomes. All content delivered using the VLE which includes a monitored discussion forum with topics created for each weeks lecture material and each problem set.
Learning requirements:
- Knowledge and understanding of statistical machine learning theory and methods for supervised learning and classification, at a level equivalent to that which would be achieved upon completion of "STAT30270 Statistical Machine Learning" (or STAT40750), or modules with similar contents and learning outcomes.
- Knowledge of data programming and data analysis at a level equivalent to that which would be achieved upon completion of "STAT30340 Data Programming with R", and modules with a relevant component of coding and implementation of statistical methods with R.
- Knowledge of linear algebra (vectors, vector spaces, matrices), calculus (derivatives), and function optimization.
- Knowledge of regression analysis and linear models, including multiple linear regression. - Understanding of statistical inference (confidence intervals, hypothesis testing, etc.) and familiarity with standard probability distributions (Gaussian, binomial, etc.).
Group/class feedback, post-assessment.
Prof Cert in Data Science