Basic Information
The Course Deals With Basic Methods For Processing and Analyzing Information With Deterministic and Probabilistic Tools. Course Topics# Characterization of Discrete Signals and Systems, Fourier Analysis For Linear Shift Invariant Systems, Representation And Processing of Information in Algebraic Spaces, Probabilistic Estimation, Concepts in Random Signals, Defining Convolution, Random Noise, Parametric Models and Statistical Estimation of Random Processes. Linear Prediction and Adaptive Algorithms. Proof Of Optimality and Uniqueness of Truncated Fourier Representations, Approximating The Clustering Problem, and Introduction to Functional Mappings. Learning Outcomes# On Successful Completion of The Course The Student Should Know# 1. Characterize a Linear Shift Invariant System And Link Its Analysis to The Frequency Domain 2. Introduce Data Processing As Matrices Representing Linear Systems and Vectors Representing Signals. 3. Operate a Statistical, Parametric and Direct Valuation Tool For a Variety of Basic Problems in Information Processing. 4. Run Linear Operators to Clear Noisy Data With Known Statistical Properties.
Faculty: Computer Science
|Undergraduate Studies
|Graduate Studies
Pre-required courses
44131 - Signals and Systems or 104174 - Algebra Bm or 234125 - Numerical Algorithms
Course with no extra credit
236200 - Signal 236327 - Digital Image and Signal Processing
Semestrial Information
Weekly Hours
3 Academic Credit • 2 Lecture Hours • 1 Discussion Hours • 1 Project Hours
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Responsible(s)
Ron Kimmel
Exams
Session A: 03-09-2024 Session B: 08-10-2024Registration Groups
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Weekly Hours
3 Academic Credit • 2 Lecture Hours • 1 Discussion Hours • 1 Project Hours
Go to Course Page
Responsible(s)
Alfred Bruckstein
Exams
Session A: 11-04-2024- אולמן 203. 206.
Registration Groups
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Weekly Hours
3 Academic Credit • 2 Lecture Hours • 1 Discussion Hours • 1 Project Hours
Go to Course Page
Responsible(s)
Alfred Bruckstein
Registration Groups
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