EE640 Spring 2010 Class Schedule

Last updated: 5-1-10

Bolded, Underlined, Italic Text is NOT YET UPDATED.

Room RMS 323	11:00am-12:15pm	11:00am-12:15pm
Month	Tuesday	Thursday
1=January		(14) Lecture: Course description, organization. Historical perspective of probability and Stochastics
1	(19) Lecture: Set Theory, Sets & Conditional Prob.	(21) Lecture: Combinatorial Probability, binomial and bernoulli distributions
1	(26) Lecture: random variables, pdf and cdf. Gaussian and uniform r.v. HW#1A Due: Prob. Set theory, 2.1, 2.4, 2.5 w/conditional prob. Problems of interest: 2.1,2.2, 2.3,2.4,2.5		(28) Lecture: Expected Value, Continuous r.v., dirac delta function, conditional, joint and marginal. HW#1B Due: 2.8=switching network, 2.11 mass func., 2.12 Problems of interest: 2.6,2.7,2.8, 2.9, 2.12
2	(2) Lecture: characteristic function and moment generating. HW #2A Due: 2.14, 2.15, 2.18 = expected value. Problems of interest 2.13, 2.14, 2.15, 2.16, 2.18	(4) Lecture: Multivariate Gaussian Random vectors, mean vector, covariance matrix and function of one random variable. HW #2B Due: 2.19 = expected value, 2.25 Gaussian moments, 2.28 = characteristic function. MATLAB VISUALIZATION Form 4 images, each is 256x256. The first matrix is a filled with values from a uniform distribution U(0,1). The second is a binarized matrix from the first with the threshold at 0.5 value. The third matrix is binarized from the first with a threshold 0.20 and the fourth matrix is a Gaussian distribution with mean 0 and variance 1. Problems of interest 2.19, 2.20, 2.22, 2.23, 2.25, 2.27, 2.28
2=February	(9) Lecture: Bivariate Gaussian and correlation coefficient. HW #3A Due: 2.29 conditional E{}, 2.31 joint Gaussian.2.32 variance Problems of interest 2.29, 2.30, 2.31, 2.32	(11) Lecture: Functions of more than one r.v. Functions of more than one r.v. continuded. Jacobian and auxillary variables.
2	(16) Lecture: Functions of more than one r.v. Central Limit Theorem. HW #3B Due: 2.33 functions of r.v.,2.37 functions of r.v.s, 2.39 functions of 2 r.v.s VISUALIZATION: Stationary Noise Visualization (see main web page for description). Problems of interest 2.33, 2.34,2.35,2.36,2.39,2.41	(18) Lecture: Bounds and convergence Lecture: Random Process Introduction. HW #4A Due: 2.42 Covariance matrix, 2.43 Conditional covariance, 2.45 eigenvalues. Problems of interest 2.42, 2.43, 2.44, 2.45, 2.46, 2.47
2	(23) Random Process Statistics HW #4B Due:2.46 eigenvalues, 2.47 multivariate Gaussian. Project 1A, part A.1	(25) Types of R.P. Types of stationarity HW #5A: 2.48 bound, 2.49 TI and CB, 2.51 Union Bound Problems of interest 2.48, 2.49, 2.50, 2.51
3	(2 ) Quantization noise model. HW #5B: 2.55 l.i.m. convergenc, 2.57 Gaussian Approx, 2.59 practical. Problems of interest 2.31, 2.46, 2.47, 2.55, 2.56, 2.57,2.58, 2.59, 2.62	(4) EXAM I A: covers chapter 2, open book, open notes, NO communication devices. (TA proctored).
3=March Midterm	(9) Lecture: Wiener-Kitchene Theorem, PSD and cross-cov, cross-correlation. Stochastic differentiation and integration.	(11) Lecture: Stochastic Integration and Differentiation, Linear Systems HW #6A: 3.4 Markov, 3.9 Random walk is Martingale, 3.10 Poisson. Problems of interest 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9,3.10
3	(16) SPRING BREAK	(18) SPRING BREAK
3	(23) Lecture: Discussion of project and non-stationary noise visualization. HW# 6B: 3.11 WSS, 3.12 WSS, 3.14 autocorrelation. Problems of interest 3.11,3.14,3.15 Project 1A, part A.2	(25) Lecture: Stochastic Series, KL, Expansion. HW# 7A: 3.16 PSD, 3.18 autocorrelation function, 3.24 bandwidth. Problems of interest 3.16, 3.17, 3.18, 3.19, 3.21, 3.23, 3.24, 3.41, 3.43, 3.46 Project 1A, part A.3
3	(30) Lecture: Binary Hypothesis Lecture: Maximum Likelihood Ratio, discussion of next visualization. Fisher Discriminant	(1) Lecture: HW# 7B: 3.41 Time Averages, 3.43 Basis Vectors, 3.46 Quantization. Visualization : Non-Stationary Colored Noise
4=April	(6) EXAM IB & IIA (TA proctored). Combined EXAM I B and EXAM II A: covers graded HW up to this date, open book, open notes, NO communication devices.	(8) NO CLASS
4	(13) Lecture: Due Wednesday: HW #8A: 4.2 LTIVC, 4.5 LTIVC, 4.8 System PSD. Due Wednesday: Project 1B Problems of interest: 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.8, 4.9, 4.12, 4.14, 4.17, 4.18, 4.20	(15) Lecture: HW #8B: 4.16, 4.18 PSD bandwidth, 4.20 response from PSD
4	(20) Binary Detector and orthogonal decision space HW #9A: 6.1 MAP decision, 6.7 cost optimization, 6.10 Neyman-Pearson Problems of interest: 6.1, 6.2, 6.3, 6.4, 6.6, 6.8,6.10, 6.11, 6.12, 6.14, 6.18, 6.19, 6.20	(22) EXAM II B: covers material not on Exam II A and up to graded HW. Open Book, Open Notes, closed Comm. Devices. HW#9B: 6.12 ROC, 6.15 minimum Probability of error, 6.19 M-ary MPE.
4. Dead Week	(27) TAKEHOME Redo of problem 1 of exam IB due. HW#10A: Visualization: Correlation Detection Performance in Non-stationary Colored Noise Problems of interest:7-6, 7-8, 7-10,7-36,7-40, 7-41	(29)
5. Finals Week	(3) Monday everything is due: (4) HW 10B optional and with 10A will replace 2^nd lowest HW grade. HW#10B: 7-6, 7-8 MMSE, 7-40 Wiener Project 1C: Due Monday	(May 6)