Aims:
This is an Industrial Project course that consists of two parts: Work-based Learning (WBL) and an Industrial Project (IP).
The WBL part of the course will deliver work-based blended on-line learning materials, with the following 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.
The Industrial Project part of the course will require the student to implement an industrial project, embedded in and contextualised for the host company, focussing on the practical machine learning techniques learned in the WBL part of the course.
Detailed Information
Pre-requisite course(s): F29AX Artificial Intelligence and Intelligent Agents (GLA) .
Location: Edinburgh.
Semester: AY.
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
- 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.
- Can relay/apply learned knowledge to work based computing projects.
Learning Outcomes: Personal Abilities
- Ability to select appropriate approaches to address given problems in an industrial context
- Awareness of distinctive features of industrial practice
- Suitably robust preparation of testing strategies.
- Reflection on system development and performance.
- Can identify, define, and analyse alternative project scenarios
- Take significant responsibility for their work and for a range of resources
- Can communicate effectively with work colleagues on learned issues
Assessment Methods:
Assessment: Coursework 100%
SCQF Level: 10.
Credits: 15.