Calendar
This section provides the lecture topics for the course. This course also has a final exam, which is not included on the calendar.




LEC # 



TOPIC 







1 



Introduction and overview 







2 



Probability models and axioms 







3 



Conditioning and Bayes' rule 







4 



Independence (problem set 1 due) 







5 



Counting 







6 



Discrete random variables; probability mass functions; expectations (problem set 2 due) 







7 



Conditional expectation; examples 







8 



Multiple discrete random variables (problem set 3 due) 







9 



Continuous random variables  I 







10 



Continuous random variables  II (problem set 4 due) 












Quiz 1, 50 minutes 







11 



Continuous random variables and derived distributions 







12 



More on continuous random variables, derived distributions, convolution 







13 



Transforms (problem set 5 due) 







15 



Iterated expectations, sum of of a random number of random variables 







16 



Prediction; covariance and correlation (problem set 6 due) 







17 



Bernoulli process 







18 



Poisson process (problem set 7 due) 







 



Quiz 2, 50 minutes 







19 



Poisson process examples 







20 



Markov chains  I 







21 



Markov chains  II (problem set 8 due) 







22 



Markov chains  III 







23 



Weak law of large numbers (problem set 9 due) 







24 



Central limit theorem 







25 



Strong law of large numbers (problem set 10 due) 







26 



Decision theory 







27 



TBA 



