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