Show/hide contentOpenClose All
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 using various evaluation metrics; 4) be able to use Python and scikit-learn for machine learning tasks using real datasets.
Indicative Module Content:Classification Techniques including kNN, Decision Trees, Naive Bayes and SVM
Regression
Gradient Descent and Neural Networks
Ensembles
Evaluation Methodology and Measures
Introduction to Reinforcement Learning
Unsupervised learning techniques including dimensionality reduction, partitional and hierarchical clustering
Running machine learning tasks using Python/Scikit-learn
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Tutorial | 24 |
Autonomous Student Learning | 80 |
Total | 128 |
This module requires strong mathematical ability, as some of the algorithms require some understanding of linear algebra and statistical concepts.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Assignment: Assignment 1: Applying ML techniques in some simplified real-world application | Unspecified | n/a | Graded | No | 20 |
Examination: A final exam to test the theoretical understanding of machine learning concepts | 2 hour End of Trimester Exam | No | Alternative linear conversion grade scale 40% | No | 60 |
Assignment: Assignment 2: Applying ML techniques in some simplified real-world application | Unspecified | n/a | Graded | No | 20 |
Resit In | Terminal Exam |
---|---|
Spring | No |
• Feedback individually to students, post-assessment
Not yet recorded.
Name | Role |
---|---|
Dr Deepak Ajwani | Lecturer / Co-Lecturer |
Mr Ryan O'Connor | Tutor |
Lecture | Offering 1 | Week(s) - 2, 3, 4, 6, 8, 9, 10, 12 | Mon 13:00 - 13:50 |
Lecture | Offering 1 | Week(s) - 5, 7, 11 | Mon 13:00 - 13:50 |
Tutorial | Offering 1 | Week(s) - Autumn: All Weeks | Thurs 14:00 - 15:50 |
Lecture | Offering 1 | Week(s) - Autumn: All Weeks | Wed 12:00 - 12:50 |