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. 

Topics include: 

• 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.