Machine Learning (2025-2026)

Course term: Michaelmas
Course lecture information: 8 lectures
Course level: M
Assessment type: Written Essay
Course overview:

This course provides a comprehensive introduction to machine learning, covering key con­cepts such as supervised and unsupervised learning, accuracy computation, and neural networks. Through a mathematical lens, students will learn fundamental ML techniques and explore some real-world applications in biological modeling.
Course synopsis:
1.    Introduction to machine learning
2.    Supervised learning
3.    Unsupervised learning
4.    Accuracy metrics 
5.    Basics on Neural Networks
6.    Mathematical foundations of machine learning
7.    Real-world applications of machine learning
8.    Practical examples using Python (& project discussion)