Computational Physics II module (PH51001)

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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. 



This module is available on following courses: