In order to achieve the goal, two main tasks are set, which students achieve by learning the courses included in the module:
1. To provide knowledge and understanding of natural language processing problems and methods of solving them, so that students can independently use open source LT, conduct research related to LT, as well as develop new, practical LT solutions (DatZ1373).
2. To provide knowledge about the structure and use of deep machine learning solutions, to develop practical skills in the use of deep machine learning frameworks, so that students can independently develop solutions for typical machine learning tasks - text and image classification, text and image processing (DatZ3299).



Fundamentals of Natural Language Processing (DatZ1373)

Lecturers: professor Inguna Skadiņa and associate professor Normunds Grūzītis 
The aim of this course is to introduce students to the language technology and its use in practical applications and to provide knowledge about the fundamentals of natural language processing – the key issues and solutions.
The course covers the basic methods as well as the most important innovations and trends in the field of language technology. This includes language processing and modeling at different levels of text analysis by applying both knowledge-based and data-driven approaches.
The main focus is on data-driven methods and the language resources they require. We will primarily consider the aspects of processing the English and Latvian languages, using open-source libraries, language models and toolkits for practical solutions.
The objectives of this course are: 1. To introduce students to the main problems and fundamental methods of natural language processing. 2. To widen students' knowledge and understanding about the importance and role of language technology in computer science and for society in general. 3. To provide theoretical and practical knowledge for the use and integration of existing language technology solutions and for the development and evaluation of new language technology solutions.

Fundamentals of deep machine learning (DatZ3299)

Lecturer: associate professor Pēteris Paikens
The aim of this course is to provide an overview of modern applications of machine learning and develop practical skills in using deep neural networks for common machine learning tasks - classification, text and image processing.
The objective of this course is to provide an introduction into artificial neural network based models, as well as an introduction to existing API frameworks for training such models. The practical assignments will be developed in Python programming language with PyTorch framework.
Learning the course does not require prior knowledge of machine learning, but Python programming language skills are required, in which practical work will be carried out using the PyTorch framework.