| Course title | Mathematical Modeling of Text 2 |
|---|---|
| Course code | KOL/VMMT3 |
| 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 |
|
Practical and theoretical introduction: - Optimization, gradients, objective and loss functions - Data vectorization, normalization and scaling Practical applications with theory: - Task types, activation functions, output processing and linking to loss-functions - Multilayer feedforward networks, concepts - Convolutional networks, concepts - Use of pre-trained models (transfer learning) - Special architectures (autoencoders and applications, multi-head networks) - Recurrent networks - Transformers, GPT Training and evaluation pitfalls: - Model evaluation, detection and interpretation of re-learning status, underlearning - Regularization (L1, L2, dropout), batch normalization - Data augmentation
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| Learning activities and teaching methods |
| unspecified |
| Learning outcomes |
|
The aim of the course is to further extend the acquired knowledge of machine learning to artificial neural networks and deep learning, which are the basis of artificial intelligence. Students will gradually learn about the issues of training neural networks, from optimization techniques, target/loss function definitions, data preprocessing methods, to the pitfalls and practices of model training and evaluation. Forward multilayer networks (including convolutions) with applications to text and image processing will be presented. Space will also be devoted to special architectures.
Create an artificial neural network to solve a given data, text, image, video processing problem in Python. |
| Prerequisites |
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unspecified
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| Assessment methods and criteria |
|
unspecified
(1) Regular attendance (maximum 1 unexcused absence). (2) Final project: train three neural networks on typical problems, evaluate them completely and write a report. |
| 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 | 3 | Recommended year of study:3, Recommended semester: Winter |
| 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 | 3 | Recommended year of study:3, Recommended semester: Winter |
| 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): General Lingvistics (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 | 3 | Recommended year of study:3, Recommended semester: Winter |
| Faculty: Faculty of Arts | Study plan (Version): General Lingvistics (2022) | Category: Philological sciences | - | Recommended year of study:-, Recommended semester: - |
| Faculty: Faculty of Arts | Study plan (Version): Lingvistics and Digital Humanities (2020) | Category: Philological sciences | 3 | Recommended year of study:3, Recommended semester: Winter |
| 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): General Lingvistics (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 | 3 | Recommended year of study:3, Recommended semester: Winter |