Course Syllabus
Engineering Probability
ECSE-2500
Spring 2014
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Basic Information
▪ Course Title: Engineering Probability
▪ Course Number: ECSE-2500
▪ Credit Hours: 3
▪ Semester/year: Fall 2014
▪ Class Hours: Monday & Thursday, 2:00 – 3:20pm.
▪ Room: Darrin Communication Center 324
▪ Course LMS: http://lms.rpi.edu
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Prerequisites & Requirements
▪ Corequisite: ECSE-2410 (Signals & Systems).
▪ Note that since ECSE-2010 (Circuits) and MATH2400 (Diff-Eq) are pre-reqs for ECSE-2410, these are
therefore pre-reqs for Engineering Probability as well.
▪ Note that this means MATH-1010 (Calculus I) and
MATH-1020 (Calculus II) and “some knowledge of
matrices” are also pre-reqs. We will use this material,
so please review it!
▪ ECSE-2500 is required for both the EE and CSE
curricula.
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Instructor
▪ Prof. K.S. Vastola
▪ vastola@ecse.rpi.edu
 Best way to reach me.
▪ Office: JEC 6003
▪ 518-276-6074
▪ Office Hours: TBD
▪ TA OH: TBD.
▪ Do you prefer the day HW is due (Mon, Thurs) or
the late afternoon before (Sunday, Wednesday)?
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Catalog Course Description
Axioms of probability, joint and conditional
probability, random variables, probability density,
mass, and distribution functions, functions of one and
two random variables, characteristic functions,
sequences of independent random variables, central
limit theorem, and laws of large numbers. Applications
to electrical and computer engineering problems.
Corequisite: ECSE-2410. Offered fall and spring terms
annually.
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Course Goals and Objectives
▪ To understand basic probability theory and
statistical analysis and be able to apply these to
modeling typical computer and electrical
engineering problems such as noisy signals,
decisions in the presence of uncertainty, pattern
recognition, communication network traffic, and
digital communications.
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Student Learning Outcomes
▪ Be able to apply basic probability theory.
▪ Be able to apply concepts of probability to model
typical computer and electrical engineering
problems.
▪ Be able to evaluate the performance of engineering
systems with uncertainty.
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Course Assessment Measures
▪ Regular Homework (20%).
▪ Two In-Class Exams (25% each).
▪ One Final Exam (30%).
▪ Tentative dates (let me know soon if you have a
problem with these dates):
Exam 1: October 9 (Thursday).
Exam 2: November 6 (Thursday).
Final Exam: Scheduled by Registrar during
exam week.
No make-up exams.
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Assignment Rules
▪ Homework almost every class.
▪ Homework assignments must be handed on the due
date at the beginning of the lecture or not at all. No
late HW allowed.
▪ Two lowest (or missing) HWs will be dropped.
▪ Discussing homework is encouraged, but each
student must prepare a separate solution.
▪ No copying! (from each other or from online)
▪ All exams will be closed book, with one sheet of
notes allowed (then 2, then 3).
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Course Textbook 1
Probability, Statistics, and Random Processes for Electrical
Engineering, 3rd Ed., A. Leon-Garcia, Pearson, 2008.
Topics by Chapter (time permitting)
Chapter 1: Probabilistic Modeling, Experiments and Outcomes.
Chapter 2: Sample Space, Events, Axioms of Probability,
Combinatorics, Conditional Probability, Statistical
Independence, Sequences of Experiments.
Chapter 3: Discrete Random Variables, Probability Mass
Functions, Cumulative Probability Distribution, Expected
Value and Moments.
 Here, we need basic infinite series from Calc I.
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Course Textbook 2
Topics by Chapter (continued)
Chapter 4: Continuous Random Variables, Probability Density
Functions, Cumulative Distribution Function, Functions of a
Random Variable, Expectation.
 Need 1-D integration & differentiation from Calc I.
Chapter 5: Pairs of Random Variables, Joint, Conditional and
Marginal Probability Distribution and Density Functions,
Independence of Two Random Variables, Covariance and
Correlation, Bivariate Normal Distribution.
 Need 2-D integration & differentiation from Calc II.
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Course Textbook 3
Topics by Chapter (continued)
Chapter 6: Vector-valued Random Variables, Jointly Gaussian
Random Vectors.
 Need basic vectors & matrices from Calc II.
Chapter 7: Sample Mean and Law of Large Numbers, Central
Limit Theorem.
 Need limits from Calc I.
Chapter 8: Statistics: Samples and Sampling, Parameter
Estimation, Confidence Intervals.
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Course Textbook 4
Recommended 2nd Text
Schaum's Outline of Probability, Seymour Lipschutz,
Marc Lipson, McGraw-Hill Professional Publishing,
2011.
▪ Lots of worked problems.
▪ Perhaps even more than other courses, you must do
in order to learn in this course. This book is very
helpful to work out problems.
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Academic Integrity
▪ Student-teacher relationships are built on trust.
▪ Acts which violate this trust undermine the educational
process.
▪ The Rensselaer Handbook of Student Rights and
Responsibilities defines Academic Dishonesty and you should
make yourself familiar with this.
▪ In this class, you may work with fellow students to get ideas
and hints, but all assignments that are turned in for a grade
must represent your own work.
▪ If you have any question concerning this policy before
submitting an assignment, please ask for clarification.
▪ Cheating will result in a grade of F for the course and a report
to the Dean of Students Office.
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