## Lectures

Lecture 2: Linear dimensionality reduction

Lecture 3: Principal component analysis (PCA)

Lecture 4: Linear prediction, maximum likelihood, regularization and cross-validation

Lecture 5: Probability review and intro to Bayes

Lecture 6: More on Bayes and regression

Lecture 7: Optimization and logistic regression

Lecture 9: The Monte Carlo Method

Lecture 10: Information, computation, Energy, Dynamical systems and Boltzmann machines

Lecture 11: The Mathematics of restricted Boltzmann machines

Lecture 12: Clustering and Mixture models: K-means and the EM algorithm

Lecture 13: Gaussian processes, active learning, bandits and Bayesian optimization

Lecture 14: Ensemble methods: Boosting and random forests

Lecture 15: Bayesian networks, factored graphs, and conditional random fields

## Useful Links :

- Deep Learning Website.
- Geoff Hinton's Website.
- Yehuda Koren's Website.
- Yann Lecun's Website.
- Andrew Ng's Website.
- Jason Weston's Website.
- Russ Salakhutdinov's Website.
- The book of Kevin Murphy.
- Hastie, Tibshirani and Friedman: The elements of statistical learning.
- Machine learning video lectures
- Why stats: NYTimes article
- The following handout should help you with linear algebra revision: PDF