Course co-ordinator(s): Dr Ben Kenwright (Edinburgh), Dr Dongdong Chen (Edinburgh), Gavin Abercrombie (Edinburgh), Dr Neamat El Gayar (Dubai), Radu-Casian Mihailescu (Dubai).
Aims:
In this course, students will develop:
- An understanding of the fundamental concepts and techniques used in data mining and machine learning.
- An understanding of the mathematics underpinning data mining and machine learning.
- A critical awareness of the appropriateness of different data mining and machine learning techniques and the relationships between them.
- Familiarity with common applications of data mining and machine learning techniques.
Detailed Information
Course Description: Link to Official Course Descriptor.
Pre-requisite course(s): F29AI Artificial Intelligence and Intelligent Agents or basic knowledge of AI concepts and issues.
Location: Dubai, Edinburgh.
Semester: 1.
Syllabus:
Basic Concepts: datasets, dealing with missing data, classification, supervised vs unsupervised learning.
Generative Models: naïve Bayes, probabilistic graphical models, cluster analysis (such as k-means clustering, EM algorithm).
Discriminative Learning: linear regression, decision tree learning, perceptron, advanced models such as multi-layer perceptron and deep learning architectures.
Learning Outcomes: Subject Mastery
Understanding, Knowledge and Cognitive Skills Scholarship, Enquiry and Research (Research-Informed Learning)
- Extensive understanding of the data mining process and machine learning algorithms.
- Detailed understanding of the mathematics underpinning the data mining and machine learning methodologies.
- Critical awareness of the appropriateness and performance of the different techniques, as well as the relationships between them.
- Critical awareness of data quality and the appropriate use of data mining and machine learning for decision making
- Ability to apply this knowledge for practical data mining and machine learning purposes
The students will develop their research abilities, and in particular:
- Ability to conduct quantitative and qualitative research on real-life, complex data sets
- Ability to ask own research questions about the hidden properties of data
- Ability to ask own research questions about suitability of certain machine learning methods and algorithms for the given data
- Demonstrate originality and creativity in the application of knowledge
Learning Outcomes: Personal Abilities
Industrial, Commercial & Professional Practice Autonomy, Accountability & Working with Others Communication, Numeracy & ICT
The students will be expected to:
- Show capacity for rational problem identification and definition.
- Show capacity for critical analysis and solution selection, deal with complex issues and make informed judgements.
- Use appropriate computer software to process data, and to support and enhance the research tasks.
- Demonstrate the ability to learn independently and demonstrate leadership/initiative in tackling research problems.
- Manage time, work to deadlines, and prioritise workloads.
- Use a wide range of resources to present results in a way that demonstrates a good understanding of the technical and broader issues of data mining and machine learning.
- Communicate with peers and more senior colleagues
SCQF Level: 11.
Credits: 15.