Aug 25:	Introduction to the course, Relative frequency, Deterministic and 
	probabilistic models (Sec 1.2 and 1.3), Examples (Sec 1.5)
 
Aug 27: Statistical regularity, relative frequency (Sec 1.3), Discrete 
	and continuous sample spaces (Sec 2.1), Axioms of probability
	(Sec 2.2)

Aug 28: Results based on axioms of probability (Sec 2.2), random number 
	generators (Sec 2.7)

Sep 03: Probability law assignment in discrete and continuous sample 
   	spaces (Sec 2.3)

Sep 04: Counting methodes: Sampling with replacement and with ordering, 
	Sampling without replacement and with ordering, Sampling without 
	replacement and without ordering, Sampling with replacement and 
	without ordering (Sec 2.3).

Sep 08: Conditional probability, Theorem of total probability, Bayes's 
	rule (Sec 2.4)

Sep 10: Independence of events (Sec 2.5)

Sep 11: Sequential experiments, Sequence of independent experiments,
	Binomial law, (Sec 2.6)

Sep 15: Sequence of independent events: Multinomial law, Geometric law;
	Sequence of depenent events (Sec 2.6)

Sep 17: Sample statistics (mean, median, mode, variance, standard 
	deviation), Introduction to random variables (Sec 3.1)

Sep 18: Cumulative Distribution Function, properties of CDF, probability 
	mass function (Sec 3.2)

Sep 22: Probability density function, properties of pdfs (Sec 3.3)

Sep 24: (Fire drill) Conditional pdfs and CDFs (Sec 3.3), Bernoulli and 
	Binomial random variables (Sec 3.4)

Sep 25: Sec 3.4: Geometric random variable, memoryless propery, Poisson
	random variable (Sec 3.4)

Sep 29: Poisson distribution, its approximation to the Binomial
	distribution, Continuous distributions: uniform and exponential
	(Sec 3.4)

Oct 01: Memoryless property of exponential distribution, Gaussian random 
	variable, change of variables, Q and phi functions (Sec 3.4)

Oct 02: Gamma random variable (Sec 3.4), Introduction to functions of
	random variables, Dicrete functions of random variables (Sec 3.5)

Oct 06: Functions of random variables: finding the pdf directly (Sec 3.5)

Oct 08: Functions of random variables: a general method (Sec 3.5)

Oct 09: Expected value of a random variable, Properties of the 
        expected value (Sec 3.6)

Oct 14: Expectation of functions of random variable, Second moment and
        variance of a random variable (Sec 3.6)

Oct 15: Markov and Chebyshev inequalities (Sec 3.7), Transform Methods
    	(Sec 3.9)

Oct 16: Methods to generate random numbers (Sec 3.11), Exam review

10-20	MIDTERM
10-22	Chi-square [3.8]
10-23	Chi-square test (cont'd) 

10-27	reliability [3.10]   (skipped 3.11, 3.12)
10-29	reliability, multiple random variables [4.1]
10-30	2-D pdf and CDF [4.2]

11-3	Joint pmf: independent variables: 3x3 example [4.3]
11-5	Joint and conditional pmf, dependent variables: 3x3 example [4.4]
11-6 	Dependent variables: f(x,y) = x+y.: joint, marginal, conditional pdf, CDF [4.4]

11-10	Review for MT2
11-12	Conditional pdf, conditional expectation, many dependent variables [4.4 and 4.5]
11-13	MT2

11-17	Function of two variables (sum, product, quotient) 4.6
11-19	Linear transformations (4.6), Expected values of fns of rv's (4.7)
11-20	Jointly Gaussian Variables (4.8)

11-24	Mean Square Estimation (4.9) (skip 4.10)
11-26/27	TXG

12-1	Laws of Large Numbers (5.1, 5.2)
12-3	Central Limit Theorem (IMPORTANT)
12-4	Review