SubjectsSubjects(version: 845)
Course, academic year 2018/2019
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Selected Problems in Machine Learning - NPFL097
Title in English: Vybrané problémy ve strojovém učení
Guaranteed by: Institute of Formal and Applied Linguistics (32-UFAL)
Faculty: Faculty of Mathematics and Physics
Actual: from 2018 to 2018
Semester: winter
E-Credits: 3
Hours per week, examination: winter s.:0/2 C [hours/week]
Capacity: unlimited
Min. number of students: unlimited
State of the course: taught
Language: Czech, English
Teaching methods: full-time
Additional information:
Guarantor: RNDr. David Mareček, Ph.D.
Class: Informatika Mgr. - volitelný
Classification: Informatics > Computer and Formal Linguistics
Annotation -
Last update: Mgr. Barbora Vidová Hladká, Ph.D. (01.02.2019)
The seminar focuses on deeper understanding of selected 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 (Chinese restaurant process, Pitman-Yor process, Gibbs sampling) and implementation of these methods on selected tasks. Further topics are selected according to students' interest.
Course completion requirements -
Last update: RNDr. David Mareček, Ph.D. (24.04.2019)

To get the credit, students are required to implement (and deliver in time) three programming assignments. Missing points can be obtained by a presentation of selected machine learning method or machine learning task.

Literature -
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,

Last update: RNDr. David Mareček, Ph.D. (24.04.2019)

1. Introduction

2. Beta-Bernouli and Dirichlet-Categorial models Beta-Bernouli Dirichlet-Categorial Beta distribution

3. Modeling document collections, Categorical Mixture models, Expectation-Maximization Document collections Categorial Mixture Models

4. Gibbs Sampling, Latent Dirichlet allocation Gibbs Sampling Latent Dirichlet allocation Latent Dirichlet Allocation

5. Text segmentation Bayessian inference with Tears Unuspervised text segmentation

6. Finding motifs Finding Motifs in DNA Finding motifs in DNA

7. Inspecting Neural Networks

8. Sentence Structures

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