MATH 218
Probability and Random Processes
Tentative Syllabus
COURSE
SEMESTER
INSTRUCTOR
E-MAIL
CLASS SCHEDULE
OFFICE AND PHONE
Teaching Assistant
OFFICE HOURS
Math 218 Probability and Random Processes
SPRING 2007
Asst. Prof. Dr. Mahmut Ali GÖKÇE
ali.gokce@ieu.edu.tr
Wed. 08:30-11:20 ; Wed. 12:30-15:20
409; 488 8465
Umut Avci (Check his office hours)
Wednesday 15:30-16:30, Thursday 10:30-12:30
COURSE OBJECTIVES
This Course aims to provide basic and some further concepts of probability and random
processes including the axioms of probability, Bayes' theorem, random variables and sums of
random variables, law of large numbers, the central limit theorem and its applications,
confidence intervals, discrete and continuous-time random processes, elementary introduction
to queueing theory.
TEXTBOOK Your basic textbook is “Probability and Stochastic Processes: A Friendly
Introduction for Electrical and Computer Engineers, Roy D. Yates and David J. Goodman
REFERENCE BOOK
COURSE GRADING
Course grades will be based on a weighted average based on the following:
Midterm Exam
Quizes/ Assignments
Participation
Final Exam
PERCENT
90-100
85-89
80-84
75-79
70-74
65-69
60-64
50-59
49 and below
30%
15%
%5
50%
GRADE
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
LETTER
AA
AB
BB
BC
CC
CD
DD
DF
FF
COURSE OUTLINE
WEEK
1
CHAPTER
1
2
1
3
2
4
2
5
2
6
3
7
3
8
4
9
10
4
11
5
12
6
13
10
14
10
PAGES
TOPIC
Experiments, Models and Probabilities: Probability
Axioms, Conditional Probability
Independence, Tree Diagrams, Counting Methods,
Independent Trials
Discrete Random Variables: Probability Mass Functions,
Cumulative Distribution Function, Averages, Functions
of Random Variables
Families of Discrete Random Variables: Bernoulli,
Geometric, Binomial, Pascal, Poisson, Discrete Uniform
Expected Value, Variance and Standard Deviation,
Conditional Probability Mass Function
Continuous Random Variables: Probability Density
Function, The Cumulative Distribution Function,
Expected Values
Families of Continuous Random Variables: Uniform,
Exponential, Erlang Gaussian Random Variables, Mixed
Random Variables, Delta Functions, Derived Random
Variables, Conditional Distributions
Sums of Random Variables: Expected Values of Sums,
PDF of the Sum of Two Random Variables, Moment
Generating Functions, Random Sums
Midterm Exam
Sums of Random Variables: Expected Values of Sums,
PDF of the Sum of Two Random Variables, Moment
Generating Functions, Random Sums
Random Vectors, Marginal Probability Function,
Independence of random variables and random vectors,
functions of random vectors
Central Limit Theorem and its Applications, Markov
and Chebyshev Inequalities, Chernoff Bounds
Stochastic Processes: Definition, Examples and Types of
SPs, IID Random Sequences
The Bernoulli Processes, The Poisson Processes,
Expected Value and Correlation, Gaussian Processes
Review and Final Exam
ASSIGMENTS: Assignments are individual unless specifically stated otherwise.
RULES FOR ATTENDANCE: Attendance is required at all times. Students are expected to
come to class fully prepared to discuss textbook readings and course assignments. Some
percentage of your final grade will be based on your attendance and class participation.
HOMEWORK POLICY: Homework problems are the best preparation for exams. You
should try to work the homework problems without constant reference to the text or passively
receiving help from others. I encourage to discuss problems with others, but you should try to
do the actual problems yourself. If you have gotten the idea about how to solve a problem
from another person or by looking things up in the text, try to do a related problem without
outside aid.