Courses may be added or updated over the next few months.  Check here for interesting new courses or updates.

ESE 503-910 Simulation Modeling and Analysis

Description: This course provides a study of discrete-event systems simulation in the areas of queuing, inventory and reliability systems as well as Markov Chains, Random-Walks and Monte-Carlo systems. The course examines many probability distributions used in simulation studies as well as the Poisson process. Fundamental to most simulation studies is the ability to generate reliable random numbers and so the course investigates the basic properties of random numbers and techniques used for the generation and testing of pseudo-random numbers. Random numbers are then used to generate other random variables using the methods of inverse-transform, convolution, composition and acceptance/rejection. Finally, since most inputs to simulation are probabilistic instead of deterministic in nature, the course examines some techniques used for identifying the probabilistic nature of input data. These include identifying distributional families with sample data, using maximum-likelihood methods for parameter estimating within a given family and testing the final choice of distribution using chi-squared goodness-of-fit.

Instructor: Michael A. Carchidi

Activity: Lecture

Format: Online Synchronous

Meeting Dates: 5/24/2021-6/30-2021

Meeting Info: MWF 12:00 PM-2:30 PM

Generally Offered: Spring Term

Can Satisfy CPG Requirements: EE CAT A,B,C, or D or SE CAT A: System Modeling

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ESE 531-910 Digital Signal Processing

Description: This course covers the fundamentals of discrete-time signals and systems and digital filters. Specific topics covered include: review of discrete-time signal and linear system representations in the time and frequency domain, and convolution; discrete-time Fourier transform (DTFT); Z-transforms; frequency response of linear discrete-time systems; sampling of continuous-time signals, analog to digital conversion, sampling-rate conversion; basic discrete-time filter structures and types; finite impulse response (FIR) and infinite impulse response (IIR) filters; design of FIR and IIR filters; discrete Fourier transform (DFT), the fast Fourier transform (FFT) algorithm and its applications in filtering and spectrum estimation.

Instructor: Tania Khanna

Activity: Lecture

Format: Online Asynchronous

Meeting Dates: 5/24/2021-6/30-2021

Meeting Info: TBA

Generally Offered: Fall Term

Can Satisfy CPG Requirements: EE CAT A,B,C, or D or SE CAT A: System Modeling

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ENM 503-910  Introduction to Probability and Statistics

Description: Introduction to combinatorics: the multiplication rule, the pigeonhole principle, permutations, combinations, binomial and multinomial coefficients, recurrence relations, methods of solving recurrence relations, permutations and combinations with repetitions, integer linear equation with unit coefficients, distributing balls into urns, inclusion-exclusion, an introduction to probability. Introduction to Probability: sets, sample setsevents, axioms of probability, simple results, equally likely outcomes, probability as a continuous set function and probability as a measure of belief, conditional probability, independent events, Bayes’ formula, inverting probability trees. Random Variables: discrete and continuous, expected values, functions of random variables, variance. Some Special Discrete Random Variables: Bernoulli, Binomial, Poisson, Geometric, Pascal (Negative Binomial) Hypergeometric and Poisson. Some Special Continuous Random Variables: Uniform, Exponential, Gamma, Erlang, Normal, Beta and Triangular. Joint distribution functions, minimum and maximum of independent random variables, sums of independent random variables, reproduction properties. Properties of Expectation: sums of random variables, covariance, variance of sums and correlations, moment-generating function. Limit theorems: Chebyshev’s inequality, law of large numbers and the central-limit theorem. Extra Topics: Generating random numbers and simulation, Monte-Carlo methods, The Poisson Process and Queueing Theory, Stochastic Processes and Regular Markov Chains, Absorbing Markov Chains and Random Walks.

Instructor: Santosh S. Venkatesh

Activity: Lecture

Format: Online Asynchronous

Meeting Dates: 5/24/2021-6/30-2021

Meeting Info: TBA

Generally Offered: Fall Term

Can Satisfy CPG Requirements: EE CAT C: SEAS Elective or CAT D: Open Elective or SE CAT C: Technical Elective

May 24  11-Week Session Classes begin

May 24  Session I Classes begin

May 31  Memorial Day Observed (no classes)

June 30  Session I Classes end

July 1  Session II Classes begin

July 5  Independence Day Observed (no classes)

August 6  Session II & 11-Week Session Classes end

Check Academic Calendar for updates.