Our capacity to fathom the world around us hinges on our ability to understand quantities which are inherently unpredictable. Therefore, in order to gain more accurate mathematical models of the natural world we must incorporate probability into the mix. This course will serve as an introduction to the foundations of probability theory. Topics covered will include some of the following: discrete and continuous random variables, 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. There is an honors version of this course: see MATH 60.
Lectures: 
M, W, F,  11:30am  12:35pm 
Kemeny 105  Tu,  12:15pm  1:05pm (Xhour) 
Office Hours: 
Monday,  4:00pm  5:00pm 
Kemeny 320  Tuesday,  3:00pm  4:00pm 
Thursday,  10:00am  11:00am 
#  Date  Topics  Suggested HW & Announcements 


1  Fri, June 23  Class Information
What is probability? Intro to counting 


2  Sat, June 24  Counting combinations 


3  Mon, June 26 
Binomial Coefficients
Events and Outcomes 


4  Wed, June 28 
Probability Laws
Conditional Probability 


5  Fri, June 30 
Independence
Bayes' Theorem Probabilistic Paradoxes 

Weekly graded homework assignments, posted above. Daily suggested homework from the textbook, not graded.
Exam 1:  Friday, July 14 
Exam 2:  Friday, August 4 
Final Exam:  Sunday, August 27, 8:00am  11:00am 
Homework:  25% 
Labs:  10% 
Exam 1:  20% 
Exam 2:  20% 
Final Exam:  25% 
June 22:  Classes begin 
June 24:  Special Saturday day of classes 
July 4:  Independence Day, no classes 
July 6:  Final day for electing use of the NonRecording Option (NRO) 
August 2:  Final day for dropping a fourth course without a grade notation of "W" 
August 9:  Final day to withdraw from a course 
August 23:  Last day of classes 
August 27:  Math 20 final exam 