Machine Learning and Intelligent Systems
This course introduces the basic concepts of Machine Learning, together with the most common families of classifiers and predictors. It identifies the basic ideas underlying the mechanism of learning, and specifies the practical problems that are encountered when applying these techniques, together with possible solutions to manage those difficulties.
By the end of the course, students will be able to decide which learning algorithm to use for what problem, code up their own learning algorithm, evaluate and debug it.
Format:
The course is a combination of lecture and lab sessions. The lectures will discuss the fundamentals of topics required to understand machine learning algorithms. During the lab sessions, students will put into practice some of the concepts seen during the lectures. Attendance to the lab sessions is mandatory and a pre-requisite to present the final exam. Throughout the term, students will also work on a related project that they present at the end of the semester.
Responsible:
Textbooks:
- The Elements of Statistical Learning - T. Hastie, R. Tibshirani & J. Friedman
- Machine Learning and Pattern Recognition - C. Bishop
Repository:
Complementary material and labs can be found in the course’s GitLab repository.