- 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.
- nosilec: Aleksander Sadikov
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.
- nosilec: Jure Žabkar