Infopaket

Special programming and simulation methods in the physics of elementary particles and nuclei

ВБ 4.3

Вибіркова дисципліна для ОП

Second

2023/2024

3 Semester

6

To know the theory and problems of modern machine learning models Know the basic approaches to regression and classification problems using neural networks Be able to use Python libraries for data preparation and metric classification tasks To be able to apply theoretical and practical approaches to multidimensional classification problems Demonstrate the ability to communicate freely in the language of instruction Use knowledge of foreign languages ​​to read technical literature on programming in preparation for seminar classes To participate in professional discussions during classroom work

Full-time form

1. Know the basics of mathematical analysis, probability theory, statistical physics and thermodynamics. 2. To be able to apply previously acquired knowledge from the courses of mathematical analysis, probability theory, statistical physics and thermodynamics to solve practical problems. 3. To have basic calculation skills from the theory of differential equations course.

Part 1. Basic methods 1 Basic concepts and the problem of retraining 2 Decision trees 3 Metric classification methods, stochastic gradient method 4 Method of support vectors. Logistic regression 5 Method of principal components. Ridge regression Part 2 Neural networks 6 Gradient boosting. A forest of decisive trees. 7 Hierarchical clustering. Partial training. 8 Categorical factors. Neural networks. 9 Unsupervised machine learning.

1. Pattern Recognition and Machine Learning, Bishop, Christopher (2006). 2. An Introduction to Statistical Learning, Gareth Games, Daniela Witten, Travor Hastie, Robert Tibshirani, Springer (2013). 3. Neural Networks A Systematic Introduction, R Rojas, Springer (1996).

Lectures Practical Individual work

Oral survey Projects Report Exam

Ukrainian