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