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Climate Change Seminar
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CC BY-NC-SA
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This course provides a broad overview of issues related to climate change, with an emphasis on those aspects most relevant to computer scientists. Topics include climate science, climate models and simulations, decision-making under uncertainty, economics, mitigation strategies, adaptation strategies, geoengineering, policy-making, messaging, and politics.The course will culminate in a presentation of a research project which might include a paper, a blog, software etc.

Subject:
Applied Science
Atmospheric Science
Engineering
Mathematics
Physical Science
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Drake, Henri
Edelman, Alan
Fernandez, John
Rivest, Ronald
Date Added:
09/01/2019
Infinite Random Matrix Theory
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CC BY-NC-SA
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In this course on the mathematics of infinite random matrices, students will learn about the tools such as the Stieltjes transform and Free Probability used to characterize infinite random matrices.

Subject:
Algebra
Mathematics
Physical Science
Physics
Statistics and Probability
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Edelman, Alan
Win, Moe
Date Added:
09/01/2004
Introduction to Computational Thinking
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CC BY-NC-SA
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This is an introductory course on computational thinking. We use the Julia programming language to approach real-world problems in varied areas, applying data analysis and computational and mathematical modeling. In this class you will learn computer science, software, algorithms, applications, and mathematics as an integrated whole. Topics include image analysis, particle dynamics and ray tracing, epidemic propagation, and climate modeling.

Subject:
Applied Science
Career and Technical Education
Computer Science
Engineering
Environmental Science
Environmental Studies
Mathematics
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Drake, Henri
Edelman, Alan
Sanders, David
Sanderson, Grant
Schloss, James
Date Added:
09/01/2020
Introduction to Computational Thinking
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CC BY-NC-SA
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This class uses revolutionary programmable interactivity to combine material from three fields -- Computer Science + Mathematics + Applications -- creating an engaging, efficient learning solution to prepare students to be sophisticated and intuitive thinkers, programmers, and solution providers for the modern interconnected online world.
Upon completion, students are well trained to be scientific “trilinguals,” seeing and experimenting with mathematics interactively as math is meant to be seen, and ready to participate and contribute to open source development of large projects and ecosystems.

Subject:
Applied Science
Career and Technical Education
Computer Science
Engineering
Environmental Science
Environmental Studies
Mathematics
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Edelman, Alan
Leiserson, Charles
Sanders, David
Date Added:
09/01/2022
Introduction to Computational Thinking with Julia, with Applications to Modeling the COVID-19 Pandemic
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CC BY-NC-SA
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This half-semester course introduces computational thinking through applications of data science, artificial intelligence, and mathematical models using the Julia programming language. This Spring 2020 version is a fast-tracked curriculum adaptation to focus on applications to COVID-19 responses.
See the MIT News article Computational Thinking Class Enables Students to Engage in Covid-19 Response

Subject:
Applied Science
Computer Science
Engineering
Mathematics
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Edelman, Alan
Sanders, David
Date Added:
02/01/2020
Matrix Calculus for Machine Learning and Beyond
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CC BY-NC-SA
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We all know that calculus courses such as 18.01 Single Variable Calculus and 18.02 Multivariable Calculus cover univariate and vector calculus, respectively. Modern applications such as machine learning and large-scale optimization require the next big step, "matrix calculus" and calculus on arbitrary vector spaces.
This class covers a coherent approach to matrix calculus showing techniques that allow you to think of a matrix holistically (not just as an array of scalars), generalize and compute derivatives of important matrix factorizations and many other complicated-looking operations, and understand how differentiation formulas must be reimagined in large-scale computing.

Subject:
Algebra
Calculus
Mathematics
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Edelman, Alan
Johnson, Steven
Date Added:
01/01/2023
Parallel Computing
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CC BY-NC-SA
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This is an advanced interdisciplinary introduction to applied parallel computing on modern supercomputers. It has a hands-on emphasis on understanding the realities and myths of what is possible on the world's fastest machines. We will make prominent use of the Julia Language, a free, open-source, high-performance dynamic programming language for technical computing.

Subject:
Algebra
Applied Science
Computer Science
Engineering
Mathematics
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Edelman, Alan
Date Added:
09/01/2011
Random Matrix Theory and Its Applications
Conditional Remix & Share Permitted
CC BY-NC-SA
Rating
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This course is an introduction to the basics of random matrix theory, motivated by engineering and scientific applications.

Subject:
Algebra
Calculus
Mathematics
Statistics and Probability
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Edelman, Alan
Win, Moe
Date Added:
02/01/2004