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Last update: doc. Mgr. Barbora Vidová Hladká, Ph.D. (25.04.2019)
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Last update: RNDr. David Mareček, Ph.D. (05.05.2022)
To get the credit, students are required to implement and deliver in time (usually three) programming assignments. Missing points can be obtained in the final test. |
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Last update: RNDr. David Mareček, Ph.D. (24.04.2019)
Christopher Bishop: Pattern Recognition and Machine Learning, Springer-Verlag New York, 2006
Kevin P. Murphy: Machine Learning: A Probabilistic Perspective, The MIT Press, Cambridge, Massachusetts, 2012
Kar Wi Lim, Wray Buntine, Changyou Chen, Lan Du: Nonparametric Bayesian topic modelling with the hierarchical Pitman-Yor processes, International Journal of Approximate Reasoning 78, Elsevier, 2016
Kevin Knight: Bayesian Inference with Tears, 2009, http://www.isi.edu/natural-language/people/bayes-with-tears.pdf |
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Last update: RNDr. David Mareček, Ph.D. (05.05.2022)
1. Introduction
2. Beta-Bernouli and Dirichlet-Categorial models
3. Modeling document collections, Categorical Mixture models, Expectation-Maximization
4. Gibbs Sampling, Latent Dirichlet allocation
5. Unsupervised Text Segmentation
6. Unsupervised tagging, Word alignment, Unsupervised parsing
7. K-means, Mixture of Gaussians, Hierarchical clustering, evaluation
8. T-SNE, Principal Component Analysis, Independent Component Analysis
9. Linguistic Interpretation of Neural Networks |