EE300syllabus Fall2019 PDF

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Course Number and Title: EE-300: Computational and Statistical Methods for Electrical and Computer Engineering San Diego State University, Georgia Fall 2019 Instructor: Tinatin Davitashvili E-mail: [email protected] Office Location: TSU, building XI (Ganivi), Room 365 Office Hours: Contact me via email with “EE-300” in subject line. Please, indicate your name and group number. Meeting in person: See me after class to choose a meeting time. Class Time/Location: ISU New Building, Room 304 Group 1 Tuesday - Thursday - 9:00 - 10:30 Group 2 Tuesday - Thursday - 11:00 - 12:30. Catalog Description: Random signals and events in electrical engineering. Introduction to basic probability, discrete and continuous random variables, joint random variables. Application of probabilistic models and concepts to engineering; data analysis and point estimation using computer-aided engineering tools. Credits: 3.0. Prerequisites: EE-210 with a grade of C or better, (prior to Fall 2012: CompE160 , Math 151). Computational Resources: You must have access to a modern version of Matlab. Required Text and Reading Materials: Applied Statistics and Probability for Engineers, by Douglas C. Montgomery and George C. Runger, 7th Edition, Wiley and Sons. Text supplemented by other Internet resources. Course format: Two lectures per week. To pass this class students must demonstrate that they meet the minimum requirements through submission of homework, quizzes, mid-term and final exams. There will be 3 main tests during the Semester – 2 Midterms (approximately in weeks #6 and #10, exact date TBA) and Final Exam. Grading Policy: 37% 20% 20% 23%

WileyPlus Homeworks and Other Assignments/Project Midterm #1 Midterm #2 Final Exam.

Course Objectives: 1. Understand basics of probability theory;

2. Calculate failure probabilities in computer systems and networks, probabilities for resource prediction in electrical and computer systems design; 3. Analyze data for short- and long-term correlation, data for periodicity and frequency; 4. Write Matlab programs to perform statistical analysis, clustering data, random number generation, signal visualization; 5. Employ statistical measures for signal quality estimation and data summarization; 6. Perform Matlab-based simulation of probabilistic systems; 7. Design experiments to collect data and estimate stochastic system parameters. Topics Covered 1. Probability. Dependent Probability. Bayes Theorem. Network Reliability; 2. Experiments, sample spaces, events, probability, enumeration and counting techniques, conditional probability, independence; 3. Simple graphical techniques (stem-leaf plots, histograms, scatter plots); 4. Introduction to Matlab and data visualization using Matlab; 5. Descriptive Statistics, Population and Sampling; 6. Sample statistics and sampling distributions; 7. Discrete random variables, probability mass functions, cumulative distributions; 8. Discrete probability distributions: Binomial, hypergeometric, and negative binomial; 9. Continuous random variables, probability density functions, cumulative distribution functions; 10. Gaussian (Normal) and other continuous probability models; 11. Noise: Gaussian, Johnson, 1/f, for electronics and comm applications; 12. Joint distributions, expectation, covariance, correlation, linear combinations; 13. Point estimation, properties of point estimators, maximum likelihood; 14. Regression analysis, linear, polynomial and others; 15. Time series, signal transformations, and signal analysis techniques. Learning Outcomes 1. Apply concepts of probability to Network Reliability, Communications, etc.; 2. Design experiments and simulate stochastic systems using a computer; 3. Use statistical analysis tools to answer practical questions about data; 4. Transform data -> information -> understanding; 5. Apply analytical math tools to solve real-world engineering problems; 6. Demonstrate ability to locate, research and apply statistical theory to large data sets; 7. Acquire and demonstrate autodidactic skills. Standards for Student Conduct: see at http://go.sdsu.edu/student_affairs/srr/conduct.aspx . Class Attendance Policies: Attendance at all scheduled sessions is mandatory unless otherwise noted via Blackboard or e-mail announcements. Unexcused absences will have a negative impact on your grade. A student who expects to be absent from classes for any reason shall notify instructors in advance and provide them a schedule indicating any class days that will be missed. A faculty member retains the right to either arrange or deny a make-up exam, quiz or lecture based on a student providing an official document proving the reason of his or her absence. Class Attendance Grading Penalties: Absent up to 2 classes: no penalty Absent 3-5 classes: - 1 letter grade (i.e. from A to A-) Absent 6-8 classes: - 2 letter grades Absent 9 or more classes: Fail. • Exceptions will be provided by the instructor for medical reasons with approved medical documentation at the Instructor’s discretion;

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All absences will be reported to the student service center at SDSU Georgia and will be taken seriously and may lead to probation or failing of a course as explained above.

Academic Integrity: Academic integrity is one of the fundamental principles of a university community. San Diego State University expects the highest standards of academic honesty from all students. Violations of academic integrity include the following: (1) unauthorized assistance on an examination, (2) falsification or invention of data, (3) unauthorized collaboration on an academic exercise, (4) plagiarism, (5) misappropriation of research materials, (6) unauthorized access of an instructor’s files or computer account, and (7) plagiarism with consequences as described in the SDSU Catalog. If Student’s academic integrity is not maintained on a test or assignment, she/he will automatically receive a grade of zero for that test or assignment, or an F in the course and will be reported to the Dean’s Office, in accordance with SDSU academic integrity policy. Penalties can be severe. The full San Diego State University Senate Policy File from July 2017 can be found at http://senate.sdsu.edu/documents/policyfile/UnivPoliciesAcademics_Aug2017bkm1.pdf...