| 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 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): 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 (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 |
| 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): 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 (2021) | 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 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 |