Software and computer complexes for high-energy physics

Course: High Energy Physics

Structural unit: Faculty of Physics

Title
Software and computer complexes for high-energy physics
Code
ВБ 3.3
Module type
Вибіркова дисципліна для ОП
Educational cycle
Second
Year of study when the component is delivered
2023/2024
Semester/trimester when the component is delivered
3 Semester
Number of ECTS credits allocated
6
Learning outcomes
1. Know the principles of neural networks, basic architectures, principles of data preparation, training and evaluation of the result 2. Practical skills in building and training neural networks.
Form of study
Full-time form
Prerequisites and co-requisites
- Successful learning of basic physics courses: "Mathematical Analysis", "Linear Algebra" "Mechanics", "Molecular Physics", "Electricity", "Optics". - Be able to solve problems in the main physics and mathematics courses. - Have the profound skills of working on a computer to search for information on the Internet. - The student must know the Python programming language and its main libraries
Course content
The course "Software and Computer Complexes for High Energy Physics" will significantly improve professional training of students of the Nuclear Physics Department, which is due to the fact that the students will be: - know the principles of neural networks construction and algorithms and methods of their training. - Know how to prepare data for training and verification of neural networks; - Have knowledge of the architectural principles of neural networks - To perform architectural arrangements for training and prediction modes in neural networks.
Recommended or required reading and other learning resources/tools
1. Deep Learning. deeplearningbook.org 2. Practical Deep Learning for Coders. https://www.fast.ai/ 3. CS231n: Convolutional Neural Networks for Visual Recognition. cs231n.stanford.edu
Planned learning activities and teaching methods
- semester assessment: 1. the assessment during the lectures (maximum 50 points). 2. Practical exercises (maximum 50 points). - Summative assessment in the form of an examination
Assessment methods and criteria
Lecture demonstration, practical classes, independent work; consultations
Language of instruction
Ukrainian

Lecturers

This discipline is taught by the following teachers

Departments

The following departments are involved in teaching the above discipline