**This course is taught in English**

This course introduces basic as well as modern concepts of statistical learning in terms of artificial neural networks (deep learning), with applications in statistical data analysis. Topics treated include feedforward networks, regularization and optimization of networks with many layers, convolutional networks, recurrent networks and validation methods. The course also includes some of the following modern topics; autoencoders, representation learning, deep generative methods, and information theoretic concepts of deep learning.

This course introduces basic as well as modern concepts of statistical learning in terms of artificial neural networks (deep learning), with applications in statistical data analysis. Topics treated include feedforward networks, regularization and optimization of networks with many layers, convolutional networks, recurrent networks and validation methods. The course also includes some of the following modern topics; autoencoders, representation learning, deep generative methods, and information theoretic concepts of deep learning.

Class Schedule

Class Schedule

**Please note that self-enrollment on the course page is not the same as course registration in Ladok.**

- Teacher: Chun-Biu Li
- Teacher: Tobias Wängberg