1. Understanding the basic concepts of neural networks
2. Ability to create solutions based on neural networks
3. Ability to adapt existing neural networks to a new problem
4. Ability to use existing software frameworks for neural networks
5. Ability to evaluate the performance of neural network based solutions  

The lectures present theoretical concepts and algorithms followed by concrete examples.

Laboratory exercises are done with computers. During the exercise, students try out theoretical concepts and apply them to specific problems

Team project in which students solve a real practical problem of biomedical image analysis

1. Perceptron (learning paradigms,Hebbian learning, competitive learning, Boltzmann learning)
2. Perceptron (learning paradigms,Hebbian learning, competitive learning, Boltzmann learning)
3. Perceptron (learning paradigms,Hebbian learning, competitive learning, Boltzmann learning)
4. Multilayer perceptron (error-backpropagation learning, credit-assignment problem, backpropagation through time)
5. Multilayer perceptron (error-backpropagation learning, credit-assignment problem, backpropagation through time)
6. Radial basis function networks (solving interpolation problem with radial basis function networks, generalized radial basis function networks, relation to regularization theory)
7. Support vector machine for classification
8. Midterm exam
9. Recurrent neural networks (Hopfield network, Hopfield network energy function, Boltzman machine, Elman networks, Jordan networks) and learning algorithms (back propagation through time, reccurent backpropagation)
10. Self-organizing networks (Hebbian non-supervised learning, Oja's learning rule, PCA using self-organizing network, Sanger's learning rule, Competitive non-supervised learning, winner-takes-all network, Kohonen's self-organizing maps)
11. Network ensembles (committee machines, mixture of experts, convolutional neural networks)
12. Deep convolutional networks: layers, architectures, visualization, fine tuning, applications, implementation
13. Deep generative models: stacked RBMs, convolutional autoencoders, variational autoencoders, adversarial models, sparsity              
14. Project
15. Final exam