Machine Learning

This microcredential will introduce students to a set of intelligent algorithms applied in modern computing. Students will also develop a theoretical and mathematical foundation of these intelligent algorithms, and show how these algorithms can be used in problem solving environments and understand their properties and limitations. Through this, they will be able to gain experience with working with these advanced algorithms.

1. To collect and clean structured and unstructured data from a variety of sources including structured databases, web services, and text-based data formats.

2. To understand key techniques for deriving association rules from frequent item sets.

3. To understand and apply a pre-existing algorithm to real datasets.

4. To design and implement the workflow required to solve key analytics challenges including recommendation engines, trading algorithms, fault detection, event detection and prediction, etc.;

5. To understand the characteristics and limitations of several different methods for categorization and grouping, and select the one most appropriate for a given task and dataset.

6. To understand and apply key algorithms such as k-Means, SVM, kNN, Naive Bayes, Active Learning, Neural Networks (supervised and unsupervised), scalable approaches including Panda and CART

A Primary Honours degree, Level 8 in Electronic/Electrical/Computer Engineering, Applied Physics, Computer Sciences or other Cognate/Engineering Disciplines. Applications are also invited from diverse educational and/or employment backgrounds, with applications evaluated on a case-by-case basis. 
And also to indicate the required documentation:

• Please provide Academic Transcripts for final year of study where appropriate (English translation)
• All applicants must submit a copy of their passport
• Please upload a CV
• If applicable, evidence of competence in the English language as per DCU entry requirements.  Please see here.