16 courses with the subject MTH, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
MTH 1111Modeling and Simulation of the Physical World2
This course provides an introduction to mathematical modeling and computer simulation of physical systems. Working with a broad range of examples, students practice the steps involved in modeling and analyzing a physical system, learn the role of models in explaining and predicting the behavior of the physical world, and develop skills with the programming and computational tools necessary for simulation. Students work in a studio environment on increasingly open-ended projects, and learn how to present their results, with an emphasis on visual and oral communication. (This course is taken with SCI1111.) MTH. Concurrent Requisites: SCI1111. Hours: 3-0-3.
Special Topics in Mathematics classes (MTH X199) typically cover a specific topic in Mathematics and are intended to enhance and expand the selection of offerings from semester to semester. Credits: Variable Credits MTH.
MTH 2110Discrete Math: Combinatorics and Graph Theory4
Discrete Mathematics is a course that will introduce students to advanced counting and partitioning techniques as well as widely applicable discrete structures such as graphs and trees. This class will emphasize creative problem solving, mathematical writing, and collaboratively carrying out small-group projects. Â MTH. Hours: 4-0-8.
An introduction to probability and statistics, with applications to science, engineering, and social science. Topics include discrete and continuous probability distributions; moments; conditional probability; Bayes' Rule; point and interval estimation; hypothesis testing. Credits: Variable Credits MTH. Hours: 2-0-4.
Data Science is a powerful toolkit for using data to answer questions and guide decision making. It involves skills and knowledge from statistics, software engineering, machine learning, and data engineering. In this class, students work on data science projects that involve collecting data or finding data sources, exploratory data analysis and interactive visualization, statistical analysis, predictive analytics, model selection and validation. Course work involves readings and case studies on ethical practice in data science. This course may be used to satisfy the Probability and Statistics requirement. MTH. Concurrent Requisites: ENGR3531.
Neurotechnology falls in the intersection of engineering, data science, and neuroscience. This area involves work in how humans can use machines to understand how we think and how to make machines that can think. Advances in neurotechnology will likely lead to new treatments for brain disorders, repair and augmentation of our sensory and motor systems, and shifts in computation strategies. In this course, students will learn about cutting-edge technologies used to understand and emulate the brain, develop statistical data analysis skills to conduct and understand neurotechnology research, and discuss the cultural and ethical implications of these advances. Course work will involve analysis of data from neuroscience, reading and synthesizing articles from research journals, and project work. MTH. Concurrent Requisites: ENGR3635.
It's not science unless you quantify your errors. Learn statistics and error analysis by studying our dynamic solar system. The first half of the class will provide you with a toolbox of standard statistical methods. You will learn these methods by studying data from planets, moons, and asteroids. The second half consists of student-designed projects. Your project will investigate an element of our solar system, and will include rigorous error analysis. This course will use data from NASA and ESA missions. MTH. Recommended Requisites Some experience with Python. Concurrent Requisites: SCI2136. Hours: 3-0-9.
Machine learning is rapidly reshaping how we live our lives. Machine learning approaches have driven recent progress in an array of technologies that have the potential to realize huge positive impacts on our world (e.g., autonomous driving, language translation, personalized recommendation, large language models, etc). However, the influence of machine learning does not end with these highly visible technologies. Machine learning algorithms are impacting our world in ways that are far less known to the general public, such as in job applicant evaluation, criminal justice, finance, politics, and medicine. The principal aim of this course is to equip students with a multi-faceted and interdisciplinary skill set to understand, implement, and critically evaluate machine learning systems. In service of this goal, students will learn the major algorithmic and mathematical frameworks that undergird modern machine learning methods. Students will learn effective processes for implementing, testing, and refining machine learning systems across a range of application domains. Students will learn how the decisions that machine learning practitioners make interact with larger societal contexts by considering factors such as transparency, fairness, bias, and privacy. MTH. Concurrent Requisites: ENGR3537. Hours: 4-0-8.
Designated Alternatives in Mathematics courses (MTH X188) typically cover mathematical topics in a required subject. Offerings under this title provide approved substitions for an Olin requirement. They vary from semester to semester and are intended to expand the selection of offerings. Credits: Variable Credits MTH. Concurrent Requisites: SCI2099.
Designated Alternatives in Mathematics courses (MTH X188) typically cover mathematical topics in a required subject. Offerings under this title provide approved substitions for an Olin requirement. They vary from semester to semester and are intended to expand the selection of offerings. Credits: Variable Credits MTH. Concurrent Requisites: SCI2099A.
Designated Alternatives in Mathematics courses (MTH X188) typically cover mathematical topics in a required subject. Offerings under this title provide approved substitions for an Olin requirement. They vary from semester to semester and are intended to expand the selection of offerings. Credits: Variable Credits MTH.
Special Topics in Mathematics classes (MTH X199) typically cover a specific topic in Mathematics and are intended to enhance andexpand the selection of offerings from semester to semester. Credits: Variable Credits MTH. Concurrent Requisites: ENGR3599B.
An introduction to the solution methods of partial differential equations that arise in describing a wide variety of problems in engineering, such as in fluid dynamics, elasticity, electromagnetic wave propagation, and transport phenomena. The course begins with the solution of boundary-value problems in ordinary differential equations (Sturm-Liouville theory), and then develops into the fundamentals of Fourier analysis and the solutions to the heat, wave, and Laplace's equations on finite and infinite domains. Additional topics will be addressed at the discretion of the instructor(s), examples of which include systems of hyperbolic equations, similarity solutions in infinite domains, or a brief introduction to numerical solutions. MTH. Recommended Requisites Linear Algebra, Vector Calculus, Differential Equations<br /><br />all are met via completion of the Quant Engr Analysis sequence. Hours: 4-0-8.
MTH 3150Numerical Methods and Scientific Computing4
The speed of modern computers has allowed simulation to become a very powerful tool in the design and analysis of systems in science and engineering. This power is easily misused and scientific computing is full of pitfalls. This course introduces students to methods useful for accurately simulating complex systems in the physical sciences and engineering. The first half of the course focuses on iterative techniques for solving algebraic systems, interpolation of functions, and advanced techniques for solutions to ordinary differential equations. The second half of the course focuses on an introduction to solutions to boundary- value problems and solutions to partial differential equations, with the students required to choose an application in science and engineering to solve in detail. MTH. Hours: 4-0-8.
This course will focus on the modern theory of dynamical systems including both discrete and continuous processes. The course will emphasize both theory and applications. Theory topics might include, for example, linear and nonlinear stability theory, periodic solutions, bifurcation theory, chaos, and strange attractors. Applications discussed might include, for example, mechanical oscillators and biological oscillators. MTH. Hours: 4-0-8.
Special Topics in Mathematics classes (MTH X199) typically cover a specific topic in Mathematics and are intended to enhance and expand the selection of offerings from semester to semester. Credits: Variable Credits MTH. OFYI - Olin First Year Introduction
