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Last update: IUUK (04.05.2015)
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Last update: Mgr. Martin Koutecký, Ph.D. (14.10.2019)
Teach the students selected topics in theory of algorithms. In the winter of 2019 we will study parameterized algorithms for integer programming. |
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Last update: Mgr. Martin Koutecký, Ph.D. (14.10.2019)
The exam is oral preceded by a written preparation. |
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Last update: IUUK (04.05.2015)
Current research papers |
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Last update: IUUK (04.05.2015)
In the fall 2015, the course will focus on machine learning.
The field of machine learning is concerned with the automatic discovery of regularities in data through the use of computer algorithms and with the use of these regularities to take actions such as classifying the data into different categories. In this course, we mostly consider the following sub-fileds of the machine learning:
(1) Supervised learning, where we have access to a known training data set. The training data comprises examples of the input vectors along with their corresponding target vectors. The goal would be to train a model using the training data in order to use the model for a test data set later. The classification and regression problems are two known examples of the supervised learning.
(2) Unsupervised learning, where we do not have access to a known training data set. The goal in such unsupervised learning problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation.
Here in this course we cover these two areas of learning in details. As an example we will speak about linear regressions, PAC learning model, EM and clustering, kernels, dimensionality reduction techniques like PCA and SVD and learning mixture of distributions among the others. We also discuss the new advances of these areas with respect to big data models such as streaming and MapReduce models. This includes new sketching and sampling techniques that have been developed very recently for supervised and unsupervised learnings when the data is big. |