Ioannis Patras, firstname.lastname@example.org
This module covers the following key concepts and themes:
Supervised Machine Learning Methods:
Unsupervised Machine Learning Methods:
It covers methods for machine learning from signals and data, including statistical pattern recognition methods, neural networks, and clustering. During the module you will learn how to:
build predictive models from previous examples
organise your data in the case that no annotation of the data is available
evaluate machine learning methods
decide which learning model is most appropriate for the problem in hand
deal with situations when few learning examples are available.
You will also learn about fundamental methodologies that drove the Machine Learning field in the last decades, such as:
linear and nonlinear regression
classification with logistic regression and Bayesian Classifiers
clustering with k-means algorithm
density estimation and dimension reduction
In addition, you will learn about state of the art methodologies, such as Deep Neural Networks and ensemble methods that are behind the recent renaissance of AI and have found applications in face detection and recognition, speech recognition and medical diagnosis, among others.