Subjects

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Neural nets in particle physics - NJSF138

Title:	Neuronové sítě v částicové fyzice
Guaranteed by:	Institute of Particle and Nuclear Physics (32-UCJF)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2015
Semester:	winter
E-Credits:	4
Hours per week, examination:	winter s.:2/1, Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	Czech, English
Teaching methods:	full-time

Guarantor:	Mgr. Tomáš Sýkora, Ph.D.
Teacher(s):	Mgr. Tomáš Sýkora, Ph.D.

Opinion survey results Examination dates WS schedule Noticeboard

Annotation -

The use of neutral networks in particle physics. For 1st year of the Master study and higher

Last update: Krtička Milan, prof. Mgr., Ph.D. (29.04.2019)

Course completion requirements -

Oral exam, which includes a presentation of a computer solution (prepared by the student) of a pre-selected problem.

Last update: Sýkora Tomáš, Mgr., Ph.D. (12.06.2019)

Literature -

Christopher M. Bishop, Neural Networks for Pattern Recognition, ISBN-13: 978-0198538646

Christopher M. Bishop, Pattern Recognition and Machine Learning, ISBN-13: 978-0387310732

Last update: T_UCJF (19.03.2015)

Requirements to the exam -

For the exam, the student prepares a computer program, on the basis of which she / he agrees with the lecturer - either the student herself / himself suggests a the topic he / she is dealing with or chooses a topic proposed by the lecturer.

In addition to the discussion of the programe solutions, the student gets three questions in the range of the lecture syllabus.

Last update: Sýkora Tomáš, Mgr., Ph.D. (12.06.2019)

Syllabus -

elements of probability theory - probability density, covariance, Bayesian definition of probability, the Gaussian distribution, data approximation

probability distribution - the beta distribution, the Dirichlet distribution, maximum likelihood of the Gaussian distribution, the Student t-distribution

linear models for regression - linear basis models, maximum likelihood and least squares, sequential learning, regularized minimal squares, bias-variance decomposition, Bayesian linear regression, limitations of linear models with fixed basis

linear classification models - discriminant functions, probabilistic discriminative models, the Laplace approximation, Bayesian logistic regression

neural nets - training, error backpropagation, the Hessian matrix, regularization in neural network, Bayesian neural networks

dual use of neural networks - approximations and decisions

Last update: T_UCJF (18.04.2012)