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[[Category:ECE]]
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[[Category:ECE302]]
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[[Category:ECE302Spring2013Boutin]]
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=[[ECE302]] Course Outline, Spring 2013, Prof. [[user:mboutin|Boutin]]=
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----
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==Part 1: Foundations (To be tested in the first intra-semestrial exam)==
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Week 1-3 (Lecture [[Lecture1_blog_ECE302S13_Boutin|1]],
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[[Lecture2_blog_ECE302S13_Boutin|2]],
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[[Lecture3_blog_ECE302S13_Boutin|3]],
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[[Lecture4_blog_ECE302S13_Boutin|4]],
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[[Lecture5_blog_ECE302S13_Boutin|5]],
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[[Lecture6_blog_ECE302S13_Boutin|6]],
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[[Lecture7_blog_ECE302S13_Boutin|7]],
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[[Lecture8_blog_ECE302S13_Boutin|8]],
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[[Lecture9_blog_ECE302S13_Boutin|9]])
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*1.1 Sets
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**Definition
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**Operations
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**De Morgan's Law
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*1.2 Probability Models
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**Sample spaces
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**Probability Laws (axioms, properties
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*1.3 Conditional Probabilities
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*1.4 Independence
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*1.5 Bernoulli Trials
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*1.6 Counting
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 +
Suggested references:
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:Chapter 1 and 2 of the textbook,
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:[http://www.athenasc.com/Prob-2nd-Ch1.pdf Chapter 1] of  "[http://www.athenasc.com/probbook.html Introduction to Probability]," by Dimitri P. Bertsekas and John N. Tsitsiklis. Athena Scientific, Belmont, Massachusetts, 2008, ISBN 978-1-886529-23-6.
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:[http://cnx.org/content/m11245/latest/ Foundations of Probability Theory: Basic Definitions], module by Don Johnson posted on [http://www.cnx.org Connexions]
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----
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==Part 2: Discrete Random Variables (To be tested in the second intra-semestrial exam)==
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Week 4-5(6) (Lecture [[Lecture10_blog_ECE302S13_Boutin|10]],[[Lecture11_blog_ECE302S13_Boutin|11]],
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[[Lecture12_blog_ECE302S13_Boutin|12]],
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[[Lecture13_blog_ECE302S13_Boutin|13]],
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[[Lecture14_blog_ECE302S13_Boutin|14]],
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[[Lecture15_blog_ECE302S13_Boutin|15]],
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[[Lecture16_blog_ECE302S13_Boutin|16]],
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([[Lecture17_blog_ECE302S13_Boutin|17]]) )
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*2.1 Definition and examples
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*2.2  Functions of a discrete random variable
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*2.3 Moments of  discrete random variable (expectation, variance)
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*2.4 Conditioning of a discrete random variable
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*2.5 Independence of discrete random variables
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Suggested References
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:Chapter 3 in the textbook
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:Chapter 2 in "[http://www.athenasc.com/probbook.html Introduction to Probability]," by Dimitri P. Bertsekas and John N. Tsitsiklis. Athena Scientific, Belmont, Massachusetts, 2008, ISBN 978-1-886529-23-6.
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:Chapter 4 of [http://cnx.org/content/col10522/latest Collaborative Statistics] by  Illowski and Dean (available online)
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----
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==Part 3: Continuous Random Variables (To be tested in the second intra-semestrial exam)==
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Week (6)7- ? (Lecture ([[Lecture17_blog_ECE302S13_Boutin|17]]) [[Lecture18_blog_ECE302S13_Boutin|18]],[[Lecture19_blog_ECE302S13_Boutin|19]],[[Lecture20_blog_ECE302S13_Boutin|20]],... )
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*3.1 Definition of continuous random variable, probability density function.
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*3.2 Moments of a continuous random variables (expectation, variance)
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*3.3 The cumulative distribution function of a random variable (discrete or continuous)
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*3.4 Normally distributed random variables.
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*3.5 Focus on 2D random variables: expectation, conditioning, and independence.
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*3.6 Function of a random variable
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*3.7 Moment Generating (Characteristic) Function (for 1D discrete/continuous random variables)
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*3.8 Pairs of jointly Gaussian Variables
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Suggested References
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:Chapter 4,5,6 in the textbook
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:This [http://www.athenasc.com/Bivariate-Normal.pdf tutorial on the bivariate normal] (from a supplement to "[http://www.athenasc.com/probbook.html Introduction to Probability]," by Dimitri P. Bertsekas and John N. Tsitsiklis. Athena Scientific, Belmont, Massachusetts, 2008, ISBN 978-1-886529-23-6).
 +
----
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==Part 4: Random Processes (To be tested in the final exam)==
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Week 11-15
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*4.1 Introduction, Definition of Random Processes (CT and DT)
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*4.2 Characteristics of Random Processes
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*4.3 Examples of DT Random Processes; Sum Processes
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*4.4 The Poisson Random Process and its relationship to Binomial Counting
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*4.5 LTI systems and Random Processes
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:Chapter 9,10 in the textbook.
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----
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[[2013_Spring_ECE_302_Boutin|Back to ECE302 Spring 2013 Prof. Boutin]]

Latest revision as of 09:18, 17 April 2013


ECE302 Course Outline, Spring 2013, Prof. Boutin

Part 1: Foundations (To be tested in the first intra-semestrial exam)

Week 1-3 (Lecture 1, 2, 3, 4, 5, 6, 7, 8, 9)

  • 1.1 Sets
    • Definition
    • Operations
    • De Morgan's Law
  • 1.2 Probability Models
    • Sample spaces
    • Probability Laws (axioms, properties
  • 1.3 Conditional Probabilities
  • 1.4 Independence
  • 1.5 Bernoulli Trials
  • 1.6 Counting

Suggested references:

Chapter 1 and 2 of the textbook,
Chapter 1 of "Introduction to Probability," by Dimitri P. Bertsekas and John N. Tsitsiklis. Athena Scientific, Belmont, Massachusetts, 2008, ISBN 978-1-886529-23-6.
Foundations of Probability Theory: Basic Definitions, module by Don Johnson posted on Connexions

Part 2: Discrete Random Variables (To be tested in the second intra-semestrial exam)

Week 4-5(6) (Lecture 10,11, 12, 13, 14, 15, 16, (17) )

  • 2.1 Definition and examples
  • 2.2 Functions of a discrete random variable
  • 2.3 Moments of discrete random variable (expectation, variance)
  • 2.4 Conditioning of a discrete random variable
  • 2.5 Independence of discrete random variables

Suggested References

Chapter 3 in the textbook
Chapter 2 in "Introduction to Probability," by Dimitri P. Bertsekas and John N. Tsitsiklis. Athena Scientific, Belmont, Massachusetts, 2008, ISBN 978-1-886529-23-6.
Chapter 4 of Collaborative Statistics by Illowski and Dean (available online)

Part 3: Continuous Random Variables (To be tested in the second intra-semestrial exam)

Week (6)7- ? (Lecture (17) 18,19,20,... )

  • 3.1 Definition of continuous random variable, probability density function.
  • 3.2 Moments of a continuous random variables (expectation, variance)
  • 3.3 The cumulative distribution function of a random variable (discrete or continuous)
  • 3.4 Normally distributed random variables.
  • 3.5 Focus on 2D random variables: expectation, conditioning, and independence.
  • 3.6 Function of a random variable
  • 3.7 Moment Generating (Characteristic) Function (for 1D discrete/continuous random variables)
  • 3.8 Pairs of jointly Gaussian Variables

Suggested References

Chapter 4,5,6 in the textbook
This tutorial on the bivariate normal (from a supplement to "Introduction to Probability," by Dimitri P. Bertsekas and John N. Tsitsiklis. Athena Scientific, Belmont, Massachusetts, 2008, ISBN 978-1-886529-23-6).

Part 4: Random Processes (To be tested in the final exam)

Week 11-15

  • 4.1 Introduction, Definition of Random Processes (CT and DT)
  • 4.2 Characteristics of Random Processes
  • 4.3 Examples of DT Random Processes; Sum Processes
  • 4.4 The Poisson Random Process and its relationship to Binomial Counting
  • 4.5 LTI systems and Random Processes
Chapter 9,10 in the textbook.

Back to ECE302 Spring 2013 Prof. Boutin

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