Course Topics
- Bias-variance 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)
- Multi-armed bandits, reinforcement learning, neural networks for Q-learning (time allowing)
Useful Links
Homeworks
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