Computational Physics II module (PH51001)
In this module, you will learn the basics of the theories behind modern machine-learning techniques. You will develop code to implement several fundamental machine-learning methods and apply them to real-world problems, such as predicting house prices and the recognition of handwriting. This module can be a basis for preparing for a career in modern machine learning – a rapidly growing field with academic and industrial applications.
- Basics of probability theory and statistics used in machine learning
- Linear regression
- Logistic regression
- Naïve Bayes models
- Support vector machines
- Deep neural networks
- Hopfield model.
This module is available on following courses: