Use Excel to solve problems needing physics, math, and basic probability.
Properties of complex numbers. Rectangular, exponential, and graphical representations of complex numbers. Euler's identity and translating between representations. Basic and advanced operations with complex numbers, such as adding, subtracting, multiplying, and dividing, as well as exp(z), ln(z), a^z, and z^a. Applying knowledge of complex numbers to linear algebra and differential equations using MATLAB.
Mathematical models for analog circuit elements such as resistors, capacitors, opamps and MOSFETs as switches. Basic circuit laws and network theorems applied to dc, transient, and steady-state response of first- and second-order circuits. Modeling circuit responses using differential equations Computer and laboratory projects. NOTE: Grades of C or better in MATH 132 and PHYSICS 152 are strongly recommended.
An introduction to using computer applications to solve engineering problems. Learning the rudiments of MATLAB and Excel, in order to design and/or visualize systems. Emphasis is on learning to use these applications appropriately and efficiently, with well structured code that is commented and includes checks to find errors.
Complex numbers. First-order differential equations. Matrices and systems of linear equations. Vector spaces and linear transformations. 2nd-order linear differential equations and the Laplace transform. Systems of differential equations.
A comprehensive introduction to computer programming with applications to various areas in electrical and computer engineering. Limited to ENGIN majors.
An introduction to computer architecture and hardware design. Topics include: computer abstractions and technology, performance evaluation, instruction set architectures, computer arithmetic, pipelining, memory systems, and interfacing. Laboratory assignments will include the use of hardware description languages, machine languages and assembly languages, and hardware emulation using FPGA boards. State-of-the-art computer simulation tools are used as part of the course.
This course covers decision making under uncertainty, focusing on topics such as evolutionary psychology, human biases, probabilistic thinking, risk taking, artificial intelligence, AI biases and algorithmic oppression. The skills learned in this class can aide students in decision making at both personal and societal levels. They can help students recognize cognitive and algorithmic biases and comprehend the social implications of these biases. Examples from everyday decisions, business/finance, economics/policy making, sports, and AI decision making are discussed. (Gen. Ed. SB, DU)
Elementary probability theory including random variables, p.d.f., c.d.f., generating functions, law of large numbers. Elementary stochastic process theory including covariance and power spectral density. Markov processes and applications.
Theory of digital circuits and computer systems stressing general techniques for the analysis and synthesis of combinational and sequential logic systems. Limited to ENGIN EE and CSE majors.