![]() ![]() If you have a question about how your exam was graded, you can ask your instructor to have your exam regraded, please submit your question in writing to your instructor. Exams are required to be submit to Gradescope. There will three midterm exams and a cumulative final exam. Office hours and problem solving sessions will be synchronous, and hopefully cover a wide range of times, so that everyone is able to participate. Lectures will be prerecorded and posted on Canvas. This course will run as a mix of asynchronous and synchronous activities. Introduction to Probability (2nd edition) by D. There is an honors version of this course: see MATH 60. Topics covered will include some of the following: (discrete and continuous)random variable, random vectors, multivariate distributions, expectations independence, conditioning, conditional distributions and expectations strong law of large numbers and the central limit theorem random walks and Markov chains. This course will serve as an introductions to the foundations of probability theory. Therefore, in order to gain more accurate mathematical models of the natural world we must incorporate probability into the mix. Our capacity to fathom the world around us hinges on our ability to understand quantities which are inherently unpredictable. ![]()
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