ECON 950 is an advanced course on econometric theory and methods. The course will deal with a variety of methods that are often referred to collectively as "machine learning" or "statistical learning".
Here is a course outline.
Much of the course will be based on the book
    Trevor Hastie, Robert Tibshirani, and Jerome Friedman, "Elements of Statistical Learning," Second Edition, Springer, 2009.
Another very useful book, which is more specialized, more up to date, and in places more technical, is:
    Trevor Hastie, Robert Tibshirani, and Martin Wainwright, "Statistical Learning with Sparsity," CRC Press, 2015.
A less advanced book, which contains a lot of useful R code, is
    Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani, "An Introduction to Statistical Learning," Springer, 2014.
Every registered upper-year student must make two short presentations or one longer one. These will typically involve discussing one or more articles or book chapters, either theoretical or empirical. Ideally, the last presentation will be a preliminary version of the student's own work, which will ultimately become an essay to satisfy the requirements of the course.
Auditors and first-year students must make one short presentation, which will involve discussing one or more articles or book chapters.
The lecture notes will, eventually, be posted here:
Failure to abide by these conditions is a breach of copyright, and it may also constitute a breach of academic integrity under the University Senate's Academic Integrity Policy Statement.
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