Infopaket

Pattern Recognition.

Обов’язкова дисципліна для ОП

Second

2021/2022

2 Semester

3

PLO1. Be able to use of in-depth professional knowledge and practical skills to optimize the design of models of any complexity, to solve specific problems of designing intelligent information systems of different physical nature. PLO2. Understanding of the principles and methods of analysis and evaluation of the range of tasks that contribute to the further development of effective use of information resources. PLO6. Be able to design and use existing data integration tools, process data stored in different systems.

To successfully learn the discipline “Complementary chapters of functional analysis. Module 1. Applied Functional Analysis” the student should satisfy the following requirements. They have successfully passed the courses Calculus and Linear Algebra. They know (a) fundamentals of pattern recognition and machine learning. They can (a) apply fundamentals of pattern recognition and machine learning to solve practical problems. They should be able to (a) seek information in the Internet.

Block 1. Bayes and linear methods Fundamentals of pattern recognition Naive Bayes classifier Bernoulli model Fisher`s linear discriminant Support vector machine Control work Block 2. Nonlinear methods Neural networks Logical regression Potential method Nonparametric classification using statistical depth Relational discriminant analysis Classification trees Quadratic discriminant analysis Control work

1. Gudfellou Ya., Bendzhio I., Kurvill A. Glubokoe obuchenie. – M.: DMK Press, 2018. 2. Fukunaga K. Vvedenie v statisticheskuyu teoriyu raspoznavaniya obrazov. — M.: Nauka, 1979. — 368 s. 3. Duda R., Hart P. Raspoznavanie obrazov i analiz stsen. — M.: Mir, 1976. — 511 s. 4. Shlezinger M.I., Glavach V. Desyat lektsiy po statisticheskomu i strukturnomu raspoznavaniyu. – Kiev: Nauk. dumka, 2004, 546 s. 5. Manning K., Raghavan P., Shyuttse H. Vvedenie v informatsionnyiy poisk. — M.: Vilyams, 2011. — 528 s. 6. Haykin S. Neyronnyie seti: polnyiy kurs — M.: Vilyams, 2006. — 1014 s. 7. Lyashko S.I., Semenov V.V., Klyushin D.A. SpetsIalnI pitannya optimIzatsIYi. — K: VPTs KNU, 2015. — 183 s. 8. Klyushin D.A. Petunin Yu.I. Dokazatelnaya meditsina. Primeneniya statisticheskih metodov. — M.: Vilyams, 2008. — 320 s. 9. Vapnik V.N., Chervonenkis A.Ya. Teoriya raspoznavaniya obrazov. Statisticheskie problemyi obucheniya. — M.: Nauka, 1974. — s. 416. ...

Lectures, laboratory works, independent work, recommended literature processing, homework.

Intermediate assessment: The maximal number of available points is 60. Test work no. 1: RN 1.1, RN 1.2 – 30/18 points. Test work no. 2: RN 1.1, RN 1.2 – 30/18 points. Final assessment (in the form of final test): The maximal number of available points is 40. The results of study to be assessed are RN 1.1, RN 1.2, RN 2.1, and RN 3.1. The form of final test: writing. The types of assignments are 3 writing assignments (2 theoretical and 1 practical).

Ukrainian