Infopaket

Topical Issues of Data Mining

ДВС.1.01

Вибіркова дисципліна для ОП

Second

2023/2024

3 Semester

4

PLO 3. To implement modern methods and techniques, namely informational, in order to perform a successful and effective professional activity and guarantee the quality of research in an applied linguistics field. PLO 17. To plan, organize, perform, and present the research and or innovative developments in an applied linguistics field. PLO 21.1. To apply professionally the knowledge of basic methods and principles of data extraction (Data Mining), an intellectual analysis of text data (Text Mining), organization and types of artificial neural networks, and types of machine learning tasks in the domain of Automatic natural language processing. PLO 22.1. To know and apply systematically the methods of analysis and applied (linguistic) industry modeling, determine the information needs and data collection to design software. PLO 27.1 See the full list of learning outcomes of the educational program in the "Program Profile" section.

Full-time form

To have basic programming skills and working with databases; To understand the concept and basic principles of automatic natural language processing systems.

The purpose of the discipline is the formation of students' professional competencies necessary for the development of data processing methods, which has become especially relevant for the current stage of development in the fields of business, politics, social processes, and general humanitarian research. The course involves studying the theoretical foundations of automatic data processing; development of practical skills in classification, deep learning, neural network technologies, machine learning, and Natural Language Processing; formation of knowledge processing skills and development of knowledge-based systems using the Python programming language (Libraries Scikit Learn and TensorFlow).

1. Artificial Intelligence in Data Mining. Theories and Applications. Edited by D. Binu. Academic Press is an imprint of Elsevier. 2021 Elsevier Inc. 271 р. 2. Alberto Artasanchez. Prateek joshi. Artificial Intelligence with Python: Your complete guide to building intelligent apps using Python 3.x. Packt Publishing; 2020. ‎ 620 p. 3. Hadelin de Ponteves. AI Crash Course: A fun and hands-on introduction to machine learning, reinforcement learning, deep learning, and artificial intelligence with Python. Packt Publishing; 2019. 534 p. 4. Tom Taulli. Artificial Intelligence Basics: A Non-Technical Introduction. Publisher: ‎ Apress; 2019. 202 p. 5. Daniel Vaughan. Analytical Skills for AI and Data Science: Building Skills for an AI-Driven Enterprise. Publisher: ‎ O'Reilly; 2020. 380 p. 6. Глибовець М., Отецький О. Штучний інтелект. К: KM Академія, 2002, 366 с. A Beginners' Guide to Visual Prolog. Version 7.2. Thomas W. de Boer. 2009.

Lectures, practical classes, independent work.

Student evaluation forms: - semester assessment: 1. Written independent works (in practical classes) 4 works are evaluated for 10 points each, a total of 40 points - the maximum number of points for a positive assessment. 2. Modular test: Two tests are evaluated: MCW1 – 10 points, MCW2 – 10 points; - the maximum number of points for a positive assessment. - final assessment: exam. The following learning outcomes are tested on the exam: The exam is written and consists of two parts: • theoretical questions (50%) • practical solution. The semester final grade is formed by the points obtained by the student in the process of performing the specified types and forms of education and obtained in the exam. The maximum distribution is carried out according to the following algorithm: 60 points (60%) - semester control and 40 points (40%) - exam (50%).

Ukrainian, English