In this module, you will learn basics of the theories behind modern machine learning techniques. During ten three-hour labs, you be asked to implement several key machine learning techniques and apply them to real-world problem, such as predicting house prices, recognition of handwriting, etc. This module can serve as a basis for preparing for a career in modern machine learning – a rapidly growing field of academic and industrial applications.
The module will cover:
· 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
Students are expected to complete assignments and will receive feedback. Students who engage sufficiently with the coursework will receive a Pass. Students who do not engage sufficiently with the coursework will not get credit.
Beyond Pass / No credit, there will be no marks for the course.