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

Institution: Dundee
Hours Equivalent Credit: 30

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.  

Link to course

Introduction to Machine Learning

Course contact

Rastko Sknepnek


20 sessions in timetable