## Lectures

Lecture 1: Introduction.
PDF
Lecture 2: The singular value decomposition.
PDF
PDF after class
Lecture 3: Applications of the SVD.
PDF after class
Lectures 4-5: Probability revision, maximum likelihood and Bayesian learning.
PDF
PDF after class
Lecture 6: Probabilistic graphical models.
PDF
PDF after class
Lectures 7-9: Linear supervised models: Least squares, ridge, and Bayesian learning.
PDF
PDF after class
Lecture 10: Optimization and Neural Networks.
PDF
PDF after class
Lectures 11-12: Boosting, K-means and EM.
PDF
PDF after class

## LATEST :

- The machine learning book of Hastie, Tibshirani and Friedman is now online: The elements of statistical learning.
- Chapters 14,15 and 20 of the artificial intelligence book Stuart Russell and Peter Norvig is strongly recommended reading for this course. I'll provide partial photocopies of chapters 14 and 15 in class. Chapter 20 is available online.
- This AIspace page at UBC has lots of videos and applets about inference in directed probabilistic graphical models (aka Bayesian networks or belief networks).
- For graphical models and Beta-Bernoulli models, I recommend A Tutorial on Learning with Bayesian Networks David Heckerman.
- Kevin Murphy has compiled a nice page about Bayesian learning.
- Wikipedia tutorial on the: SVD
- The following handout should help you with linear algebra revision: PDF
- The homework should be handed in on Wednesday at the beginning of the class. Please note that messy homeworks will be penalized - it is your responsibility to ensure that the material is presented in a clear written form. All pseudocode must be handed in. Please don't forget to add your name and student number.

## USEFUL LINKS :

- Machine learning video lectures
- Why stats: NYTimes article
- A cool company: Numenta
- A video lecture about python's package matplotlib
- The homepage of Andrew Ng