Infopaket

Problems of artificial intelligence

ДВС.3.06

Вибіркова дисципліна для ОП

First

2022/2023

8 Semester

3

PRN12. Be able to apply methods and algorithms of intelligent data analysis for the tasks of classification, forecasting, cluster analysis, finding associative rules using software tools to support multidimensional data analysis based on the use of DataMining, TextMining, WebMining technologies. PRN19.3. Know the algorithms of information analysis and be able to apply them in solving practical problems. PRN21.3. Know artificial intelligence technologies and be able to apply them in solving practical problems.

1. Know the basic methods of processing, analysis and synthesis of voice, text, symbolic forms of information, methods of modeling complex systems, methods of data visualization, etc. 2. To be able to apply the basic methods of modeling and recognizing communication information and creating software for solving artificial intelligence problems and to master practical skills in solving this class of problems. 3. Possess elementary skills in information processing, programming, databases, algorithm development, and pattern recognition.

The educational discipline consists of the following sections. Classification of artificial systems intelligence The main strategies for the development of research in the field of artificial intelligence. Problems pattern recognition and modeling of the real world. Problems and methods of classification and information clustering. Methods of data visualization and visual analytics. Problems of robotics. Methods of textual information research. Methods of voice information synthesis and recognition. Methods of building virtual human models. Research and modeling of gestural communication information. Modeling emotions on a person's face. Classification of emotions, construction of basic emotional states of a person's face. Convolutional neural networks for recognition of gestural information. Artificial intelligence methods for creating virtual world systems and human-computer interaction systems. It is taught in the 8th semester in the amount of 90 hours. (3 ECTS credits) in particular: lectures – 28 h., consultations - 2 h., independent work - 60 h. 2 control papers and a credit are provided.

Osnovnі: 1. Jurafsky D., Martin J.H. Speech and Language Processing. An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Second Edition. – Pearson Prentice Hall, 2009. 2. Krivonos Iu.G., Krak Iu.V., Kirichenko M.F. Modeliuvannia, analіz і sintez manіpuliatsіinikh sistem. K.:Nauk. Dumka. – 2006. 3. Vintsiuk T. K. Analiz, raspoznavanie i interpretatsiia rechevykh signalov. – Kiev: Nauk. dumka, 1987. 4. Rabiner L., Juang B.-H. Fundamentals of Speech Recognition. – PTR Prentice Hall, 1993. 5. Rutkovskaia D., Pilinskii M., Rutkovskii L. Neironnye seti, geneticheskie algoritmy i nechetkie sistemy: Per. s pol-sk. I.D. Rudinskogo. – M.: Goriachaia liniia-Telekom, 2004. – 452 s. 6. Krivonos Iu.G., Krak Iu.V., O.V. Barmak. Sistemi zhestovoї komunіkatsії: modeliuvannia іnformatsіinikh protsesіv. – Kiїv: Nauk. dumka, 2014. ..

Lectures, consultations, independent work

- semester assessment: 1. Control paper 1: РН1.1, РН1.2 – 40 points / 24 points. 2. Control work 2: РН1.1, РН2.1 – 40 points / 24 points. 3. Current assessment of lecture material: РН4.1, РН4.2 – 20 points / 12 points. Final evaluation (in the form of credit): - Credit points are defined as the sum of grades/points for all successfully evaluated learning outcomes provided by this program. - Scores below the minimum threshold level are not added. - The minimum threshold level for the total assessment for all components is 60% of the maximum possible number of points.

Ukrainian