• Bayesian methods: Gaussian processes, Dirichlet processes, MCMC methods, variational inference.
  • Deep learning: Boltzmann machines, Autoencoders, Convolutional neural networks.
  • Computational learning theory: PAC learning, VC dimension.
  • Other select topics: multi-kernel learning, multi-task learning, reinforcement learning.

Prediction: linear regression, logistic regression, LDA/QDA, nearest neighbors, evaluating goodness of fit.

Feature and model selection: cross-validation, bootstrap, filter methods, wrapper methods.

Advanced prediction: basis expansions, splines, regularization, decision trees, generalized additive models, local regression.

Combining models: bagging, boosting, random forests, ensemble learning.

Support Vector Machines: for classification, for regression, optimization, duality, RKHS (reproducing kernel Hilbert spaces).

Neural networks: fitting neural networks, overfitting and other computational challenges.