update in 2017-04-14
stay tuned…
Supervised learning. (7 classes)
- Supervised learning setup. LMS.
- Logistic regression. Perceptron. Exponential family.
- Generative learning algorithms. Gaussian discriminant analysis. Naive Bayes.
- Support vector machines.
- Model selection and feature selection.
- Ensemble methods: Bagging, boosting.
- Evaluating and debugging learning algorithms.
Learning theory. (3 classes)
- Bias/variance tradeoff. Union and Chernoff/Hoeffding bounds.
- VC dimension. Worst case (online) learning.
- Practical advice on how to use learning algorithms.
Unsupervised learning. (5 classes)
- Clustering. K-means.
- EM. Mixture of Gaussians.
- Factor analysis.
- PCA (Principal components analysis).
- ICA (Independent components analysis).
Reinforcement learning and control. (4 classes)
- MDPs. Bellman equations.
- Value iteration and policy iteration.
- Linear quadratic regulation (LQR). LQG.
- Q-learning. Value function approximation.
- Policy search. Reinforce. POMDPs.