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Curricular information is subject to change
On completion of this module, students will be able to: 1) Distinguish between the different categories of machine learning algorithms; 2) Identify a suitable machine learning algorithm for a given application or task; 3) Run and evaluate the performance of a range of algorithms on real datasets using a standard machine learning toolkit
Indicative Module Content:Supervised Learning methods:
* Decision Trees
* Nearest Neighbour algorithms
* Regression
* Bayesian Networks
Unsupervised learning methods:
* Clustering
* Recommender Systems
Student Effort Type | Hours |
---|---|
Lectures | 19 |
Computer Aided Lab | 20 |
Autonomous Student Learning | 76 |
Total | 115 |
Not applicable to this module.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Project: This project will test the practical aspects of the module's learning outcomes. The student is expected to demonstrate proficiency in analysing the question, and applying the best technique | Throughout the Trimester | n/a | Alternative linear conversion grade scale 40% | No | 35 |
Continuous Assessment: The miniQuizzes will test the student's ability to use algorithms to analyze data and cover theoretical aspects of the course | Varies over the Trimester | n/a | Alternative linear conversion grade scale 40% | No | 30 |
Examination: Final exam to test students ability to articulate and explain the theoretical and numerical aspects of the course | 2 hour End of Trimester Exam | No | Alternative linear conversion grade scale 40% | No | 35 |
Remediation Type | Remediation Timing |
---|---|
In-Module Resit | Prior to relevant Programme Exam Board |
• Group/class feedback, post-assessment
Not yet recorded.