| Course title | Mathematical Modeling of Text 2 |
|---|---|
| Course code | KOL/VMMT2 |
| Organizational form of instruction | Seminar |
| Level of course | Bachelor |
| Year of study | not specified |
| Semester | Winter and summer |
| Number of ECTS credits | 4 |
| Language of instruction | Czech |
| Status of course | Compulsory-optional |
| Form of instruction | Face-to-face |
| Work placements | This is not an internship |
| Recommended optional programme components | None |
| Lecturer(s) |
|---|
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| Course content |
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1) Machine learning in general - meaning, use, model, parameters, goals, optimization. 2) Optimization techniques: - Rough-force optimization, grid-search, random-search, - genetic and other algorithms, - gradient descent, variants and implementations, - cost function design, derivability, formalisms. 3) SVM models, LDA, k-NN, Naive Bayes, Decision Trees, Gradient Boosting: - Fundamentals of theory, implementation and use in Python. 4) Features suitable for machine learning: - Quantitative variables, feature engineering, - selection, extraction, reduction, the curse of dementia, applications of SVD, - Models and text vectorization: bag-of-words, semantics, LSA, - scaling, normalization, standardization. 5) Pragmatics of training: - Evaluating the success of models, - overfit, underfit phenomena and their detection, - Training, validation and test sets & training/test data problem. 6) Practical problem solving: - Creating a custom comment sentiment classifier, spam detector, ... 7) Creating and writing a report
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| Learning activities and teaching methods |
| Monologic Lecture(Interpretation, Training), Dialogic Lecture (Discussion, Dialog, Brainstorming), Work with Text (with Book, Textbook) |
| Learning outcomes |
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The aim of the course is to introduce the application of mathematical modelling of text in the form of machine learning using R/Python programming languages. The course will introduce the theory and practice of machine learning on a number of concrete and practical applications including creating a custom spam filter, sentiment detection of reviews, language detection, latent semantic analysis, etc.
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| Prerequisites |
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unspecified
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| Assessment methods and criteria |
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Student performance, Systematic Observation of Student, Seminar Work
(1) Elaboration and completion of assigned tasks. (2) Reading the assigned materials. |
| Recommended literature |
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| Study plans that include the course |
| Faculty | Study plan (Version) | Category of Branch/Specialization | Recommended semester | |
|---|---|---|---|---|
| Faculty: Faculty of Arts | Study plan (Version): General Linguistics and Communication Theory (2021) | Category: Philological sciences | - | Recommended year of study:-, Recommended semester: - |
| Faculty: Faculty of Arts | Study plan (Version): Lingvistics and Digital Humanities (2020) | Category: Philological sciences | 2 | Recommended year of study:2, Recommended semester: Summer |
| Faculty: Faculty of Arts | Study plan (Version): General Linguistics and Communication Theory (2019) | Category: Philological sciences | - | Recommended year of study:-, Recommended semester: - |
| Faculty: Faculty of Arts | Study plan (Version): Lingvistics and Digital Humanities (2020) | Category: Philological sciences | 2 | Recommended year of study:2, Recommended semester: Summer |
| Faculty: Faculty of Arts | Study plan (Version): General Lingvistics (2019) | Category: Philological sciences | - | Recommended year of study:-, Recommended semester: Summer |
| Faculty: Faculty of Arts | Study plan (Version): General Lingvistics (2022) | Category: Philological sciences | - | Recommended year of study:-, Recommended semester: Summer |
| Faculty: Faculty of Arts | Study plan (Version): General Lingvistics (2021) | Category: Philological sciences | - | Recommended year of study:-, Recommended semester: Summer |
| Faculty: Faculty of Arts | Study plan (Version): Lingvistics and Digital Humanities (2020) | Category: Philological sciences | 2 | Recommended year of study:2, Recommended semester: Summer |
| Faculty: Faculty of Arts | Study plan (Version): General Lingvistics and Theory of Communication (2014) | Category: Philological sciences | - | Recommended year of study:-, Recommended semester: - |
| Faculty: Faculty of Arts | Study plan (Version): Lingvistics and Digital Humanities (2020) | Category: Philological sciences | 2 | Recommended year of study:2, Recommended semester: Summer |
| Faculty: Faculty of Arts | Study plan (Version): General Linguistics and Communication Theory (2021) | Category: Philological sciences | - | Recommended year of study:-, Recommended semester: - |
| Faculty: Faculty of Arts | Study plan (Version): General Linguistics and Communication Theory (2019) | Category: Philological sciences | - | Recommended year of study:-, Recommended semester: - |
| Faculty: Faculty of Arts | Study plan (Version): Lingvistics and Digital Humanities (2020) | Category: Philological sciences | 2 | Recommended year of study:2, Recommended semester: Summer |