Sep. 03-07NO CLASSIntroductionProb. axioms
Sep. 10-14Countable ICountable IIBorel Sets
Sep. 17-21Cond. Prob.Contin. of Prob.Random vars
Sep. 24-28Joint pdfsConditional pdf1-D derived
Oct. 01-052-D derivedExpectation E[.]Variance s²
Oct. 08-12central limitCLT examplesInequalities
Oct. 15-19Covariance ICovariance IIn-D Gaussian
Oct. 22-26Cond. GaussML EstimationMAP estimat
Oct. 29-02LS estimationEstimator propsRV converg I
Nov. 05-09RV converg IIDiscrete RPs IDiscrete rps II
Nov. 12-16Bernoulli RPsPoisson RPs IPoisson rps II
Nov. 19-23Cont-time rpsII & Markov rpsThanksgiving
Nov. 26-30rand telegraphSpectral densityinterpret PSD
Dec. 03-07cross-spectralsmoothing filterinterpret WSS
Dec. 10-14Strong law #sergodic; evaluatsNO CLASS

The following was the schedule for the Fall 2000 term.

Sep. 04-08NO CLASSAxioms of Prob.Countable I
Sep. 11-15Countable IIContin. of Prob.Cond. Prob.
Sep. 18-22CombinationsRandom variablepmfs & pdfs
Sep. 25-29Joint (2D) pdfsConditional pdfDerived pdfs
Oct. 02-062D derived pdfExpectation E[.]variance,covar
Oct. 09-13Pr inequalitiescentral limit thmCLT examples
Oct. 16-20Covar matrix ICovar matrix IIn-D Gaussian
Oct. 23-27Cond GaussianMLE estimationMAP,LS estim
Oct. 30-03Estimator propsConvergence of..RV Sequences
Nov. 06-10Borel-CantelliDiscrete-time rpWSS thru H(z)
Nov. 13-17Bernoulli RPsand Poisson RPsCont-time RPs
Nov. 20-24Cont-time II rpsand Markov RPsThanksgiving
Nov. 27-01Wiener,telegrphSpectral densityInterpret,white
Dec. 04-08Wiener filteringSpectral interpretStrong Law #s
Dec. 11-15Periodogram biasCourse evaluationNO CLASS