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 and 104174 - Algebra Bm and 234125 - Numerical Algorithms)


Course with no extra credit

236200 - Signal 236327 - Digital Image and Signal Processing


Semestrial Information