Fundamentals of Machine Learning
Module code: G6061
15 credits in spring semester
Teaching method: Lecture, Laboratory
Assessment modes: Coursework
In this module, you are introduced to the important field of machine learning.
You use a systematic approach, based on the following three key ingredients:
You are introduced to both regression and classification, and your studies emphasise concepts such as model performance and learnability.
As part of this module, you learn techniques such as:
- 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.
You are also introduced to techniques for pre-processing the data (including PCA).
In this module, you adopt an example-based approach throughout.
some programming experience
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.