Fundamentals of Machine Learning (G6061)
Fundamentals of Machine Learning
Module G6061
Module details for 2022/23.
15 credits
FHEQ Level 5
Pre-Requisite
some programming experience
Module Outline
This module provides an introduction to the important field of machine learning. A systematic approach will be used based on the following three key ingredients: tasks, models and features. Students will be introduced to both regression and classification and concepts such as model performance and learnability will be emphasized. Taught techniques will include: linear regression, single and multiple layer perceptron classification, kernel-based models (including RBF and SVM), decision tree models and random forest, naïve bayes classification and k-means clustering. Techniques for pre-processing of the data (including PCA) will be introduced. Throughout, an example-based approach will be adopted.
Module learning outcomes
Demonstrate basic knowledge of several supervised and unsupervised machine learning models including multi-layer perceptron, support vector machine, random forest, K-means, and PCA.
Map machine learning models to tasks based on reasoned arguments.
Explain and exploit practical concepts such as cross-validation and learning curve.
Use machine learning toolboxes to solve classification/regression problems with real-world data, including pre-processing of the data and incorporating prior knowledge.
| Type | Timing | Weighting |
|---|---|---|
| Coursework | 100.00% | |
| Coursework components. Weighted as shown below. | ||
| Report | A2 Week 2 | 80.00% |
| Computer Based Exam | T2 Week 5 (1 hour) | 10.00% |
| Computer Based Exam | T2 Week 9 (1 hour) | 10.00% |
Timing
Submission deadlines may vary for different types of assignment/groups of students.
Weighting
Coursework components (if listed) total 100% of the overall coursework weighting value.
| Term | Method | Duration | Week pattern |
|---|---|---|---|
| Spring Semester | Lecture | 1 hour | 22222222222 |
| Spring Semester | Laboratory | 1 hour | 11111111111 |
How to read the week pattern
The numbers indicate the weeks of the term and how many events take place each week.
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