## Program

**Introduction to ML/CV and basic topics in statistical learning**- ERM, overfitting, kNN, cross-validation, classification/regression, error decomposition

**Linear predictors**- linear classifier, linear regression, logistic regression

**Basic optimization**- loss surface, computing the gradient, gradient descent, SGD, backpropagation

**Neural networks basics**- basic layers and activations, loss functions, regularizers

**Advanced optimization and training**- learning rate, momentum, Nesterov, ADAM, optimizing hyperparameters, train/validation graphs, intuitions and tips of training

**Convolutional neural networks**- convolutional layer, channels, stride, padding, advanced conv. layers, pooling layers; popular CNN architectures

**Visualizing CNNs, adversarial examples**- visualizing first layers, maximum activation of neurons, saliency maps, deep dream, adversarial examples, attacks and defenses

**Advanced module**- either generative models or RNNs