English

Every autumn semester

To give an introduction to several subdomains of machine learning and to give an orientation about fundamental methods and algorithms within these domains. To convey knowledge about breadth and depth of the domain.

* unsupervised and supervised learning, classification and regression
* neural networks, including convolutional neural networks, recurrent neural networks and deep learning
* bayesian learning
* reinforcement learning
* support vector machines, decision trees, random forests, ensemble methods
* hardware and software architectures for machine learning, parallelisation, use of GPUs

in an examination (oral or written) display basic knowledge concerning theories and methods related to the following subdomains:
unsupervised and supervised learning, classification and regression
neural networks, including convolutional neural networks, recurrent neural networks and deep learning
bayesian learning
reinforcement learning
support vector machines, decision trees, random forests, ensemble methods

complete a number of assignments based on problems related to some of the previously mentioned subdomains and demonstrating the ability to:
evaluate and prepare the data
select and train a model
evaluate the outcome and fine-tune the model

* be able to judge suitability of a given machine learning method to a given problem,
* understand limitations of applicability of machine learning methods

Lectures

Laboratory exercises

Written exam

The written examination can be replaced by an oral exam given mutual agreement

Failed, pass

EDAA01 Programming - Second Course or similar

Murphy, Kevin P.: Machine Learning, A Probabilistic Perspective.. ISBN 9780262018029.
Goodfellow, I., Bengio, Y. & Courville, A.: Deep Learning. MIT Press, 2016. ISBN 9780262035613.
Hands-On Machine Learning with Scikit-Learn and TensorFlow, Concepts, Tools, and Techniques to Build Intelligent Systems. ISBN 9781491962299.

EDAN95F

 -10-15

Professor Thomas Johansson