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
Hardware needed: For this course, a decent laptop/desktop computer should be sufficient, as long as it runs Python.
Continuous Assessment
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.
