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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 |
---|---|
Autonomous Student Learning | 76 |
Lectures | 19 |
Computer Aided Lab | 20 |
Total | 115 |
Not applicable to this module.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Quizzes/Short Exercises: Five short quizzes | Week 3, Week 4, Week 6, Week 8, Week 9 | Alternative linear conversion grade scale 40% | No | 15 |
No |
Individual Project: A project will be assigned for each student. The student starts from week 2 and works throughout the semester to complete the project. | Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11, Week 12 | Alternative linear conversion grade scale 40% | No | 25 |
No |
Exam (In-person): Standard in-person examination. | End of trimester Duration: 2 hr(s) |
Alternative linear conversion grade scale 40% | Yes | 60 |
Yes |
Remediation Type | Remediation Timing |
---|---|
In-Module Resit | Prior to relevant Programme Exam Board |
• Group/class feedback, post-assessment
• Online automated feedback
Not yet recorded.