Hi, I'm Trevor Hastie.
And I'm Rob Tibshirani.
We were students together about 30 years ago.
And we've been colleagues and friends ever since.
Now we're both professors of statistics at Stanford
University.
And we want to tell you about our new online course,
Statistical Learning.
This is the first online course we've ever offered.
And we're really excited to tell you about it.
What is statistics?
Statistics is everywhere.
Every clinical trial, every biological experiment, every
population survey--
they all need statistics to make sense of the results.
Statistics is used to extract the relevant summaries and to
determine if the findings are real or random artifacts.
Doctors use statistics to sharpen and interpret medical
images like MRIs.
This helps them to improve the diagnosis and treatment of
cancer and other diseases.
Web ad servers use predictive modeling to decide which adds
amongst a large collection are most suitable for a particular
web page, for a particular user.
They use logistic regression and have to rank the ads and
deliver one in less than 100 milliseconds.
Insurance companies use predictive modeling to
determine the risk of a potential client.
They also use statistical models to decide which claims
might be fraudulent.
Financial traders and hedge funds use statistical models
to learn an edge in the market.
Some analyze Twitter streams to detect market sentiment,
which they use as features in their models.
Voice and image recognition software used by Google and
Apple and other providers use large-scale statistical models
learned on billions of images and samples.
This course will teach you a variety of different modeling
techniques.
You will in some core methods, like linear regression,
logistic regression, and discriminant analysis.
And you'll also learn some modern approaches, such as the
lasso, cross validation, and the bootstrap for broadening
the scope of the classical methods.
You'll learn about fitting nonlinear models using spines
and additive models, and tree-based methods, random
forests and boosting.
And we'll teach you about support vector machines for
state-of-the-art classification as well
techniques for unsupervised learning, such as principal
components and clustering.
Not only are these lectures free, but you can access the
course textbook online for free as well.
See you in class.