ECE 302 Probabilistic Methods in Electrical Engineering

Spring 2015

Course Information

• Instructor:  Prof. S. Koskie

  • Email:  skoskie@iupui.edu

• Recitation Instructor:   Paul Witcher

  • Recitations:   MW 10:30–11:45am in SL 143

• Recitation Instructor:   Keerthanaa Ramesh

  • Recitation:   T noon–1:15pm in SL 143

• Lectures:   TR 14:30–5:45 am in BS 3015

• Textbook:   Probability and Stochastic Processes: A Friendly Introduction for Electrical and Computer Engineers, 3rd ed. by Roy D. Yates and David J. Goodman, Wiley, 2014.     There has been significant reordering of the material, and the number of examples and homework problems has been increased by about 35 percent in the new edition. Use the 2nd edition at your own inconvenience.

• Grading:

  • 15% Homework
  • 50% Midterm Exams (25% each)
  • 35% Final Exam (noncumulative)   Tuesday May 5th, 3:30-5:30pm

• Spring 2015 Course Information Sheet    (Updated January 13, 2015)

• Homework Assignments    due Tuesdays by 4:30pm. For Matlab problems, the solution must include both the commands, and the output. All graphs must include grid lines if appropriate, must be properly labelled, i.e. must have labels on both axes, and must have a title.
  • HW1: all even problems in sections 1.1-1.3, as assigned in class (due date 1/20/15)
    
  • HW2: all even problems in sections 1.4-1.6, as assigned in class (due date 1/27/15)
    
  • HW3: 2.1.8, 2.1.10, 2.1.12; 2.2.2, 2.2.4, 2.2.8; 2.3.2, 2.3.4; 2.4.2; 2.5.2. Note that it is not necessary to use tree diagrams to solve these problems so you need not read Section 2.1. (due date 2/3/15)
    
  • HW4: 3.2.2, 3.2.4, 3.2.8, 3.3.2, 3.3.6, 3.3.8, 3.3.10, 3.5.2, 3.5.8, 3.5.14 (due date 2/10/15)
    
  • HW5: 3.6.2, 3.6.4, 3.6.6, 3.7.2, 3.7.6, 3.7.8, 3.8.4, 3.8.6, 3.8.8, 3.4.2, 3.4.4, 3.4.8 (due date 2/17/15)
    
  • HW6: 4.2.2, 4.3.4, 4.3.6, 4.4.2, 4.4.6, 4.5.4, 4.5.6, 4.5.12, 4.6.14, 4.7.6 (due date 2/24/15)
    
  • HW7: 5.1.6, 5.2.8, 5.3.2, 5.3.4, 5.4.2, 5.5.2, 5.5.4, 8.1.4, 8.1.8, 8.3.2 (due date 3/24/15)
    
  • HW8: 8.4.8, 9.1.2, 9.2.2, 9.3.2, 9.4.6, 9.5.2, 9.5.4, 9.5.6, 9.6.2 (due date 4/02/15)
    
  • HW9: 7.2.2, 7.2.4, 7.2.6, 7.4.4, 7.4.6, 7.4.8, 7.5.2, 7.5.4 (due date 4/09/15)
    
  • HW10: 10.1.2, 10.1.4, 10.2.2, 10.2.4 (you must do 10.2.3 first -- don't just copy the solution), 10.2.8, 10.3.4, 10.5.2, 10.5.4. (due date 4/16/15)
    
  • HW11: 11.1.4, 11.1.6, 13.1.2, 13.2.2, 13.3.2 (due date 4/30/15)
    
  • Optional HW12: 13.4.2, 13.4.4, 13.4.6, 13.5.2, 13.5.4 (a) and (b) only, 13.7.2 (requires 13.2.1), 13.9.2, 13.9.4, 13.9.6 (due by noon on 5/04/15)
    

• Homework Solutions    (Updated May 4, 2015)

• Exam Solutions    (Updated April 29, 2015)

• Practice Exams    (Updated March 3, 2015)

• Some Useful Links:

  • Introduction to mathematical arguments, an excellent introduction to writing proofs. (Thanks to Prof. Michael Hutchings of U.C. Berkeley!)

  • List of Greek Letters and how to write them: (Thanks to Harry Foundalis!)

• Course Objectives:

  Upon completion of the course, students should be able to:

  • Solve simple probability problems with electrical and computer engineering applications using the basic axioms of probability.    (Chapters 1 – 5)
  • Describe the fundamental properties of probability density and mass functions with applications to single and multivariate random variables.    (Chapter 3 – 6 and 8)
  • Describe the functional characteristics of probability mass functions frequently encountered in electrical and computer engineering such as the Bernoulli, Binomial, Geometric, Poisson, and Uniform.    (Chapter 3)
  • Describe the functional characteristics of probability density functions frequently encountered in electrical and computer engineering such as the Exponential, Gaussian, and Uniform.    (Chapter 4)
  • Solve problems involving Derived Random Variables, Conditional Probabilities, and Sums of Random Variables.    (Chapters 6, 7, and 9, respectively)
  • Determine the first through fourth moments of any probability density function using the moment generating function.    (Chapter 9)
  • Calculate confidence intervals and levels of statistical significance using fundamental measures of expectation and variance for a given numerical data set.    (Chapters 10)
  • Formulate and Test Hypotheses using Maximum A Posteriori Probability Ratio Test    (Chapter 11)
  • Distinguish between random variables and random processes for given mathematical functions and numerical data sets.    (Chapters 8 and 13)
  • Determine whether a random process is ergodic or nonergodic and demonstrate an ability to quantify the level of correlation between sets of random processes for given mathematical functions and numerical data sets.    (Chapter 13)
  • Model complex families of signals by means of random processes.    (Chapter 13)