General
Main content blocks
- Lecturer: Profile: Lida Kanari
Course information
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)