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Probability (University of Michigan, Winter 2018)

Course webpage


Parsiad Azimzadeh

Course description

This is a fairly rigorous introduction to probability theory with some emphasis given to both theory and applications, although a knowledge of measure theory is not assumed. Topics covered include probability spaces, conditional probability, independence, moment generating and characteristic functions, expectations, convergence of random variables, law of large numbers, central limit theorem, Markov chains, and Monte Carlo methods.

An archive of the course (including all lecture notes, assigments, solutions, TeX files, etc.) can be downloaded directly.


(Optional) Walsh, John B. Knowing the odds: an introduction to probability. Vol. 139. American Mathematical Soc., 2012.

Lecture notes


All assignments are to be handed in at the beginning of class.


You are not expected to know any material in this section for assignments/exams.


The grading scheme is as follows:

Grades are posted on Canvas.

The lowest of your assignment scores will not count towards your final grade. Midterms will be held in class. The final is on April 23rd, 1:30 - 3:30 pm in 296 Weiser Hall. All exams are closed book.

The final exam covers all lectures except for lectures 21 and 22.

Alternate final times for students who cannot make the regular time are April 25: 12:00 pm - 2:00 pm and April 27: 12:00 pm - 2:00 pm. Both alternate exams will be held in 2866 East Hall.