Deep Learning

An overview course on basic neural network models and their application to image, text, and sound processing tasks.

Feed-forward networks. Matrix-vector differentiation. Automatic differentiation.
Optimization methods for neural networks. Regularization and DropOut.
Batch normalization, convolutional neural networks.
Semantic segmentation. Object detection.
Neural network image stylization.
Recurrent neural networks.
Reinforcement learning. Q-training. The DQN model.
Algorithms of Reinforce, A2C.
Generative-adversarial networks.
Variational auto-encoder.
Parameterization methods. Variational auto-encoder with discrete variables.

5 practical tasks and 1 lab work. Each task is evaluated from 10 points, and the lab - from 5 points. The exam is evaluated from 10 points.

0.7 * semester points + 0.3 * exam points.

Machine learning, statistics, optimization methods.