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 2nd lowest HW grade.

HW#10B: 7-6, 7-8 MMSE, 7-40 Wiener

Project 1C: Due Monday

(May 6)