Course Topics
 Biasvariance tradeoff, linear regression, ridge regression, lasso regression, logistic regression
 Regularization, stochastic gradient descent, minibatch gradient descent
 Kernel smoothing methods, Gaussian process regression
 Decision trees, random forests, bagging, boosting, Adaboost, gradient boosting
 Unsupervised learning, clustering, Gaussian mixtures
 Basic neural network architectures, backpropagation
 Universal approximation theory for neural networks
 Deep learning, convolution neural networks, convolution filters, pooling, dropout, autoencoders, data augmentation, stochastic gradient descent with momentum (time allowing)
 Implementation of neural networks for image classification, including MNIST and CIFAR10 datasets (time allowing)
 Multiarmed bandits, reinforcement learning, neural networks for Qlearning (time allowing)
Useful Links
Homeworks
