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Courses Random Signal Class

  1. Mar 7, 2016 #1
    Hi All,

    Looking for Advice on taking Random Signals Class in the summer, I am EE student, Is it very difficult? very challenging or demanding?. I have taken classes in the summer in a community college and I have done OK(B), however I am a little unsure about this one. I work full time and have family.. What are your thoughts, How difficult it is.
  2. jcsd
  3. Mar 7, 2016 #2
    It's no more difficult than any other course in the EE curriculum. What's the focus of the course? When you say random signals, are you referring to an introductory course in random processes for electrical engineers, or something higher-level?
  4. Mar 8, 2016 #3


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    Science Advisor
    Gold Member

    The difficulty of such a class can vary significantly from school to school and even from professor to professor, so we would just be guessing. If you post the syllabus (and textbook) our guesses might be better.

    I would recommend you speak with your advisor and with students who have taken the class.

    By the way, 20+ years ago I took a 'random signals' class (had prerequisites of probability theory, and signals and systems) and have found it to be incredibly useful. Your mileage may vary.

  5. Mar 8, 2016 #4
    I am currently taken Signals and Systems I and I am doing Ok, the university allows to take these two classes concurrent, But I decided not to.. Seems like Summer could be a good option since does not have lab. Below is the course description and books requirement. I would say is an introductory course for EE

    thanks in Advanced


    EEE 350 Random Signal Analysis (3) [F, S, SS] Course (Catalog) Description: Probabilistic and statistical analysis as applied to electrical signals and systems. Lecture. Required course. Pre or corequisite: EEE 203 Textbook: Yates and Goodman, Probability and Stochastic Processes, second edition, Wiley, 2005

    Course Topics: 1. Axiomatic probability 2. Random variables, distribution functions, and density functions 3. Special distributions: Gaussian, exponential, etc. 4. Expectation and variance 5. Multiple random variables 6. Central limit theorem and law of large numbers 7. Maximum-likelihood estimation and confidence intervals 8. Random processes 9. Statistical analysis using sample statistics, histograms, and linear regression
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