F2009 Syllabus for EE 5531 (Probability and Stochastic Processes)

Class Time: MWF 11:15am-12:05pm, MechE 212

Instructor:

John C. Kieffer
Room 6-179 EE/CS
612-625-8574
kieffer@umn.edu

Teaching Assistant:

Guido Gioberto
Room 2-276 EE/CS
612-626-7217
giobe002@umn.edu

Office Hours:

1:00 to 2:00, Mon, Wed, Thurs, 6-179 EE/CS (and by appointment)
3:00 to 4:00, Fri, 2-276 EE/CS
(also you can see the instructor "open door" when he is in his office MWF afternoons; otherwise, outside of office hours, you can send e-mail questions)

Prerequisite:

EE 3025 or equivalent. (That is, a 15 week junior-level undergraduate course in probability (75%) and random processes (25%).)
Matlab proficiency. (That is, at least equivalent to the Matlab instruction covered in EE 3015-3025.)
Remarks: At the Study Materials Web Page, you may examine the posted EE 3025 Notes and Worked Problems in order to see what we cover in EE 3025. You will also find posted there some Matlab materials, to let you know what the U of M EE undergrads do with Matlab in EE 3015-3025. It is not recommended that you take EE 5531 without the proper background. You will probably discover within the first 2-3 weeks of the course whether you have the proper background or not, so that you can adjust your enrollment status in case it is necessary to do so.

Textbook:

John A. Gubner, PROBABILITY AND RANDOM PROCESSES FOR ELECTRICAL AND COMPUTER ENGINEERS, Cambridge Univ. Press, 2006. (Some copies of the textbook are being placed on reserve at Walter Library Reserve Room and at Math Library in Vincent Hall.)

Grading:

Homework (10%)

Exam 1, Wed, Oct 14, 11:15am - 12:05pm (27.5%)

Exam 2, Wed, Nov 18, 11:15am - 12:05pm (27.5%)

Final Exam, 10:30am - 12:30pm, Saturday, December 19 (35%)

Course Outline:

EE 3025 Review
Overview of EE 5531
Convergence Theory

Review of CLT (Section 4.3, 5.6)
Strong LLN (Section 14.3)
Weak LLN for WSS Processes (Chap 14)

Random Vectors, Gaussian Random Vectors (Chapters 8,9)
Markov Processes (Chapter 12)
Second Order Theory of Random Processes (Chapter 10)
Special Types of Random Processes (Chapter 11, Section 15.5)
Ito Integral (Sections 11.3, 13.4, and Notes)

Applications
- Design of Kalman Filter (Notes)
- Predictor Design: Levinson/Durbin Algo and Infinite Memory Predictors (Notes)
- Stability of M/M/1 Queue driven by Poisson Process (Notes)
- Solving Laplace PDE using Wiener Process (Notes)
Karhunen-Loeve Expansion/Detection Theory (Section 13.3, Notes)

Student Conduct:

University of Minnesota official student conduct code defines how students should conduct themselves in their classes. Inappropriate conduct includes cheating on exams, submitting homework that is not your own work, disruptive behavior, and other things. Instructors are required to deal with violations of the student conduct code according to the official mechanisms laid out in the conduct code.