EHM454
Communication Theory
SYLLABUS
Course Code: EHM454
Course Name: Communication Theory
Course Objective: This course is an introduction to probability and statistics at the bachelor level for students in computer engineering. This core course is intended to provide a solid general background in probability and statistics.
Course Instructor: Dr. Ferkan YILMAZ { ferkan at yildiz dot edu dot tr }
Course Weekly Plan
Week 1 Introduction to probability: experiments, sample space, events, counting techniques, additive and multiplicative rules, conditional probability, and Bayes’ rule.
Week 2 Discrete Random variables and probability distributions: General aspects of discrete and continuous probability distributions, joint distributions, marginal and conditional densities, and statistical independence.
Week 3 Continuous Random variables and probability distributions: General aspects of discrete and continuous probability distributions, joint distributions, marginal and conditional densities, and statistical independence.
Week 4 Functions of one or more than one random variables: Distribution function and density function methods, sum of independent Normal random variables,
Week 5 Mathematical expectation and higher order statistics: Mean, variance, covariance, correlation, moments, Kurtosis, Skewness, and Markov and Chebychev inequalities.
Week 6 Introduction to Statistics: Population and sample, parameters and statistics,
Week 7 Simple descriptive statistics: Mean, Median, Quantiles, percentiles, and quartiles, Variance and standard deviation
Week 8 Midterm (Will be announced)
Week 9 Frequency Distributions and Histograms, Standard errors of estimates, Standard errors of estimates, Interquartile range, Graphical statistics: Histogram, Stem-and-leaf plot, Boxplot, Scatter plots and time plots
Week 10 Introduction to Statistical Inference: Population and sample, parameter estimation (Method of moments, and Method of maximum likelihood), Estimation of standard errors,
Week 11 Confidence intervals: Construction of confidence intervals: a general method, Confidence interval for the population mean, Confidence interval for the difference between two means, Estimating means with a given precision
Week 12 Unknown standard deviation: Large samples, Confidence intervals for proportions, Estimating proportions with a given precision, Comparison of two populations with unknown variances
Week 13 Hypothesis testing: Type I and Type II errors (level of significance), Level α tests (general approach), Rejection regions and power, Standard Normal null distribution (Z-test), P-value, Z-tests for means and proportions
Week 14 Inference about variances: Variance estimator and Chi-square distribution, Confidence interval for the population variance, Comparison of two variances (F-distribution), Confidence interval for the ratio of population variances, F-tests comparing two variances
Week 15 Final Exam (Will be announced).
Recommended books
Baron, Michael. Probability and statistics for computer scientists. CRC Press, 2019.
Supplementary books:
Ross, Sheldon. Probability and statistics for engineers and scientists. Elsevier, New Delhi, 2009.
Scheaffer, Richard L., Madhuri Mulekar, and James T. McClave. Probability and statistics for engineers. Cengage Learning, 2010
Soong, Tsu T. Fundamentals of probability and statistics for engineers. John Wiley & Sons, 2004.
Teaching Methods
Online and Expressive,
Theoretical-Finding-Centered Discussions
Tentative Assessment and Grading Policy
Studies in semester Number Contribution (%)
Midterm Exams 1 40
Quiz 2 20
Homework Assignments 0 0
Final Exam 1 40
Total 4 100
Attendance
The absence more than 15 hours will result in failure (F0).
Course Outcomes:
At the end of this course, the student will be able to
Understand that probability can be employed / utilized to model unpredictability.
Compute probabilities by using counting / frequency techniques and various probability rules.
Compute probabilities and joint probabilities by using famous discrete or continuous probability models.
Understand and apply the Central Limit Theorem.
Compute mean, variance and correlation for a variety of probability models, and estimate them from data.
Understand how experiments are designed for collecting data from a group of subjects.
Ability to represent a data set by Histogram, Stem-and-leaf plot, Boxplot, Scatter plots and time plots.
Achieve the statistical inference and parameter estimation using samples,
Understand confidence intervals and how to compute the statistics based on samples.
Understand hypotheses tests and how to perform them for the mean.