STAC63: Probability Models
Prof Daniel M. Roy
Email: email@example.com (please include “STAC63” in your email subject line)
Office hours: Mondays 3--4pm in IC 462, or by appointment. Changes will be announced.
Email: firstname.lastname@example.org (please include “STAC63” in your email subject line)
See section on Marking below.
Mondays 7--10pm in IC 120.
There are twelve lectures: The first lecture is September 14. (Recall that September 7 is Labour Day.) There is no lecture on October 12 due to Reading Week. The last MONDAY lecture is November 30, but the LAST LECTURE is Thursday, December 3 (making up for September 7.)
This course continues the development of probability theory begun in STAB52. Probability models covered include branching processes, birth and death processes, renewal processes, Poisson processes, queuing theory, random walks, and Brownian motion.
I will use a mixture of the course website (http://danroy.org/teaching/2015/STAC63/) and Blackboard to post material and announcements.
Each student’s grade in the course will be based on:
The following is a tentative outline of the material we will cover:
Students with diverse learning styles and needs are welcome in this course. Please feel free to approach me or Accessibility Services so we can assist you in achieving academic success in this course. If you have not registered with the Accessibility Services and have a disability, please visit the Accessibility Services website at http://www.accessibility.utoronto.ca for information on how to register.
Assignments are to be done by each student individually. You may discuss assignments in general terms with other students, but the work you hand in should be your own. In particular, you should not leave any discussion with someone else with any written notes (either paper or electronic). You may not use any resources/aids other than the book and Wikipedia. If you are not certain whether a resource is allowed, email the instructor.
Assignments are due in class by 7:08pm on the date marked, unless stated otherwise on the assignment. Late assignments will not be accepted. In the case that an official Student Medical Certificate is presented in lieu of an assignment, the missed assignment will not be factored into the student’s grade.
The required textbook is
Sheldon M. Ross (2011), Introduction to Probability Models, 11th edition, Academic Press.
I have requested that a copy be put on 3-hour reserve in the library and that the bookstore order copies for purchase. The book is also available in digital PDF format via the library.
There are many other excellent resources for learning about probability models. The following list contains just a few, some of which are freely available:
Programming assignments can be completed in any language you like, although the assignment handouts and supporting files will generally only be provided in one language (usually R, MATLAB, or Python). To be an effective statistician/data scientist, you must know how to program and manipulate data! Being able to work efficiently with all of the key data science languages is necessary in industry and in applied graduate work.
The R language (http://www.R-project.org) is free software. All public lab machines have R installed. (See http://cran.r-project.org/doc/manuals/R-intro.pdf for an introduction. There are many other resources online.) If you have access to a machine where you can install software, you might consider R Studio (http://www.rstudio.com/products/rstudio/), which is an integrated development environment (IDE) that provides many useful features.
The MATLAB language is proprietary but used throughout machine learning. MATLAB 2013 is available in the public Window labs, although you will need a version that has the Statistics toolkit installed to run many packages. Other core machine learning languages include Python and C/C++. Some up and coming languages that have gained a lot of interest within machine learning include Scala and Julia.