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HU Credits:
5
Degree/Cycle:
1st degree (Bachelor)
Responsible Department:
Computer Sciences
Semester:
1st Semester
Teaching Languages:
Hebrew
Campus:
E. Safra
Course/Module Coordinator:
Prof. Yuval Kochman
Coordinator Office Hours:
By appointment
Teaching Staff:
Prof Yuval Kochman
Mr. Royi Jacobovic
Course/Module description:
The course will cover basic topics in multivariate statistics and stochastic processes. Topics include: random Gaussian vectors, covariance matrix diagonalization, optimal estimation, linear estimation, Markov chains, definition of stochastic processes, autocorrelation, stationarity, ergodicity, Poisson processes, Gaussian processes, power spectrum, optimal linear filtering.
Course/Module aims:
Provide tools for understanding stochastic processes that appear in engineering applications, focusing on their mathematical foundations.
Learning outcomes - On successful completion of this module, students should be able to:
Analyze different stochastic processes that are common in science and engineering (Poisson, Gaussian, Markov). Understand optimal prediction for these processes, and analysis in the frequency domain.
Attendance requirements(%):
0
Teaching arrangement and method of instruction:
Lectures
Course/Module Content:
NA
Required Reading:
NA
Additional Reading Material:
A. Leon-Garcia: Probability, Statistics and Random Processes for Electrical Engineering, Prentice Hall, Third Edition.
A. Papoulis and S. U. Pillai: Probability, Random Variables and Stochastic Processes, McGraw Hill, Fourth Edition.
S. M. Ross: Introduction to probability models, Academic press
Grading Scheme :
Additional information:
NA
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