The seminar focuses on deeper understanding of selected unsupervised machine learning methods for students who have already have basic knowledge of machine learning and probability models. The first half of the semester is devoted to methods of unsupervised learning using Bayesian inference (Dirichlet-Categorical models, Mixture of Categoricals, Mixture of Gaussians, Expectation Maximization, Gibbs sampling) and implementation of these methods on selected tasks. Other lectures will be devoted to clustering methods, componet analysis and inspecting deep neural networks.
The lectures in Czech are given on Tuesdays, 12:20 - 13:50 in S1 (fourth floor)
The lectures in English are given on Wednesdays 12:20 - 13:50 (write me an e-mail if interested)
Students are expected to be familiar with basic probabilistic and ML concepts, roughly in the extent of:
In the second half of the course, you should be familiar with the basics of deep-learning methods. I recommend to attend
Unsupervised segmentation of texts in languages which does not use spaces between words.
All the necessary information the second assignment is covered by the tutorial from the last lecture:
The unsupervised segmentation is decribed in sections 17 and 29, however, you will need many other hints from the whole text, so please, read it all.
You can also go through the following slides:
Deadline: Apr 14 23:59 10 points
Deadline: May 5 23:59 10 points
Deadline: Sep 15, 23:59