This first course in Linear Algebra will introduce students to key concepts of the field, including but not limited to vectors, vector norms and inner products, matrices, matrix-vector and matrix-matrix multiplication, matrix inverses, solving systems of linear equations, vector spaces, orthogonality, least-squares, eigenvalues and eigenvectors, singular value decompositions, and principal component analysis. These theoretical tools will be grounded in exciting problems from the sciences, engineering, machine learning, data science, logistics, and economics. Through application-based case studies, you will be shown how to model problems using linear algebra and how to solve the resulting problem using standard Python scientific computing modules. Enrollment in this course assumes students have comfort with programming at the level of CIS 1100 (Python).
This course introduces students to basic concepts of thermodynamics, fluid mechanics, and heat transfer, with emphasis on applications. The course will focus on first law of thermodynamics, mass and momentum conservation for both closed and open systems. Students will be exposed to the different modes of heat transfer (conduction, convection, and radiation) with attention to conduction and convection applications to heat engines and devices. Hydrostatics, including pressure distribution and forces acting on submerged surfaces, and buoyancy effects will be discussed as how they are related to hydraulic applications. Fluid dynamics will cover inviscid flows, Bernoulli equation, and concepts of lift, drag, and thrust, and how these are related to aerodynamical systems including wind turbines. Introduction to internal flows, head loss in pipes, friction factors, and Moody chart.
This course is primarily intended for students in mechanical engineering, but may also be of interest to students in materials science and other fields. It continues the treatment of statics of rigid bodies begun in MEAM 1100/PHYS 0150 and progresses to the treatment of deformable bodies and their response to loads. The concepts of stress, strain, and linearly elastic response are introduced and applied to the behavior of rods, shafts, beams and other mechanical components. The failure and design of mechanical components are discussed. Students should have either taken MATH 2400 in a previous semester or be taking it concurrently with this course.
This is the first of a two semester sophomore level laboratory sequence that students complete over the fall and spring semesters. The course teaches the principles of experimentation and measurement as well as analysis and application to design. This fall semester course follows closely with MEAM 2020 and MEAM 2100, involving experiments to explore the principles of statics and strength of materials and thermo-fluids and energy. Prerequisite: Sophomore standing in engineering
Algorithms and Artificial Intelligence have become ubiquitous in the 21st century. From the movies recommended by Netflix to the advertisements presented on social media and the routes suggested by Google Maps, AI and algorithms can make our lives more convenient. But what about AI that that can earn a B+ on an MBA exam without studying, phones that unlock with facial recognition that doesn’t work smoothly on all skin colors, or autonomous weaponized drones that mistake civilians for targets? As algorithms play an increasing role in various aspects of modern society, addressing their ethical considerations becomes increasingly crucial to ensure their responsible and beneficial use. This course explores the ethical dimensions and implications inherent in algorithms and their associated technologies in a wide variety of contexts. Topics will range from the intricacies of privacy invasion and the mitigation of bias to the establishment of accountability in the use of algorithms in fields such as education, healthcare, finance, criminal justice, employment, environmental issues, urban planning, and weapons of war. We will critically analyze academic research, policy debates, and case studies to develop a nuanced understanding of the ethical considerations surrounding algorithms. Students will engage with cutting-edge scholarship and contribute to ongoing discussions on algorithmic ethics. As part of the course, students will interact with AI and report on their findings.