This book is written in a manner that's accessible for both statistician and engineer. It mentioned the fundamental statistical concepts without needing too much mathematical detail, and provides example codes in R language for each section.
The course is given by Alex Smola, a physicist turned computer scienctist. Alex's training in mathematics and physics allowed him to be both intuitive and precise when giving lecture. He has great insight on the topics of machine learning and their statistical aspects.
The website contain Schwannden's past lecture in linear algebra, multivariate Gaussian, regression, calculus of variation, and Gaussian process.
Singular value decomposition lays the foundation for principle component analysis, and it is a great linear algebra concepts that has many application in both bio-informatics and image processing.
This short book on proabability is a concise introduction to the probabilistic concept you will use in machine learning.
This short book on statistics is a concise introduction to the statistical concept you will use in machine learning.
This is the slide for our talk on linear model (1/22).
This is the slide for our talk on time series (1/25).
This is the slide for our talk on EM and variational inference (2/3).
This is the slide for our talk on model selection (2/6).
This is the slide for our talk on sampling method (2/9).
This is the slide for our talk on bootstrap (2/18).