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.
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.
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.
- Awareness of the appropriateness and performance of the different techniques, as well as the relationships between them.
- 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
Learning Outcomes: Personal Abilities
Industrial, Commercial & Professional Practice Autonomy, Accountability & Working with Others Communication, Numeracy & ICT
The students will be expected to:
- Demonstrate the ability to learn independently.
- Show capacity for rational problem identification and definition.
- Show capacity for critical analysis and solution selection.
- Manage time, work to deadlines, and prioritise workloads.
- Use appropriate computer software to process data.
- Present results in a way that demonstrates a good understanding of the technical and broader issues of data mining and machine learning.
Assessment: Examination: (weighting – 50%) Coursework: (weighting – 50%)
SCQF Level: 10.