We're still actively developing this site. If you encounter any issues, please report them! - Report an issue

SCIENTIFIC COMPUTING AND MACHINE LEARNING FOR ENGINEERING APPLICATIONS

ME 737
Course Description

Key computational topics for engineering applications will be discussed, encompassing both established classical numerical methods and the emerging field of machine learning. Knowledge of calculus [such asMATH 221], linear algebra and differential equations [such asMATH 320], probability [such asMATH 331] and programming in Python or MATLAB [such asCOMP SCI 220] is required.

Prerequisties

Graduate/professional standing

Satisfies

This course does not satisfy any prerequisites.

Credits

3

Offered

Not Applicable

Grade Point Average
Not Reported

Could not calculate change

Completion Rate
Not Reported

Could not calculate change

A Rate
Not Reported

Could not calculate change

Class Size
Not Reported

No change from Historical

No data available

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

Similar Courses

COMPUTING CONCEPTS FOR APPLICATIONS IN ENGINEERING

ME 459

An overview of computing concepts that support modeling and simulation in engineering applications. Learn the basics of computer architecture, software development and the interplay between software and hardware components.

INTRODUCTION TO SCIENTIFIC COMPUTING FOR ENGINEERING PHYSICS

EMA/EP 476

Background for professional numerical computation in Linux environments begins with shell scripting and software archiving. Programming skills in a compiled language are then developed through scientific and engineering examples. Engineering problem-solving skills are reinforced through applications that require numerical solutions to systems of differential and/or integral equations, while motivating progressively more advanced computational methods.

MATHEMATICAL FOUNDATIONS OF MACHINE LEARNING

COMPSCI/ECE 761

Mathematical foundations of machine learning theory and algorithms. Probabilistic, algebraic, and geometric models and representations of data, mathematical analysis of state-of-the-art learning algorithms and optimization methods, and applications of machine learning. Knowledge of probability [such asMATH/​STAT  431orCOMP SCI/​E C E  561] and linear algebra [such asMATH 341orM E/​COMP SCI/​E C E  532] is required.

HIGH PERFORMANCE COMPUTING FOR APPLICATIONS IN ENGINEERING

COMPSCI/ECE/EMA/EP/ME 759

An overview of hardware and software solutions that enable the use of advanced computing in tackling computationally intensive Engineering problems. Hands-on learning promoted through programming assignments that leverage emerging hardware architectures and use parallel computing programming languages. Students are strongly encourage to have completed COMP SCI 367 orCOMP SCI 400or to have equivalent experience.

MACHINE LEARNING IN ACTION FOR INDUSTRIAL ENGINEERS

ISYE 521

Principles, algorithms, and industrial engineering applications of machine learning. Predictive analytics, with a focus on combining data and models to improve decision-making. Methods include: statistics, linear regression, logistic regression, regularization, over-fitting, clustering, classification and regression trees, boosting, bagging, deep learning, and neural networks. Applications areas include: healthcare, transportation, and the public sector.