Course co-ordinator(s): Dr Katya Komendantskaya (Edinburgh), Dr Diana Bental (Edinburgh).
Aims:
- To introduce students to the fundamental concepts and techniques used in data mining and machine learning.
- To develop a critical awareness of the appropriateness of different data mining and machine learning techniques.
- To provide familiarity with common applications of data mining and machine learning techniques.
Detailed Information
Pre-requisite course(s): F29AI Artificial Intelligence and Intelligent Agents or basic knowledge of AI concepts and issues..
Location: Edinburgh.
Semester: 1.
Syllabus:
Data Mining: Basic concepts (datasets, dealing with missing data, classification, statistics), regression analysis, cluster analysis (k-means clustering, hierarchical clustering), unsupervised learning, self-organising maps, naïve Bayes, k-nearest-neighbour methods
Machine Learning: decision tree learning, ensemble methods (bagging and boosting, random forests), deep learning architectures, support vector machines
Learning Outcomes: Subject Mastery
Understanding, Knowledge and Cognitive Skills Scholarship, Enquiry and Research (Research-Informed Learning)
- A good understanding of the data mining process.
- A good appreciation of the mathematical basis of machine learning.
- Critical awareness of the appropriateness and performance of different techniques.
Learning Outcomes: Personal Abilities
Industrial, Commercial & Professional Practice Autonomy, Accountability & Working with Others Communication, Numeracy & ICT
- Ability to select appropriate approaches to address given problems
- Suitably robust preparation of testing strategies.
- Reflection on system development and performance.
Assessment Methods:
Assessment: Coursework: (weighting – 100%)
Re-assessment: None
SCQF Level: 10.
Credits: 15.