Machine Learning, Modeling, and Simulation Principles

Course 1 of 2 in the program Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI

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  • START DATE September 20, 2021 More Dates
  • TIME COMMITMENT 4-6 hours per week
  • DURATION 5 weeks
  • FORMAT Online
  • PRICE $1,350

WHAT YOU WILL LEARN

Understand the computational tools used in engineering problem-solving in course 1 of this 2-course program. 

View the week by week schedule here.

This course is also offered in Spanish. Enrollment is managed through our collaborator, Global Alumni

  • Simulate physical processes using numerical discretization methods.
  • Assess cost-accuracy trade-offs in numerical simulation.
  • Learn powerful optimization techniques and understand their fundamental role in machine learning.
  • Describe canonical machine learning problems from a statistical perspective.
  • Practice real-world forecasting and risk assessment problems using Monte Carlo simulation.

WHO SHOULD ENROLL

  • Industry professionals with at least a bachelor's degree in engineering (e.g., mechanical, civil, aerospace, chemical, materials, nuclear, biological, electrical, etc.) or the physical sciences.

  • Other technical professionals with a background in college-level mathematics including differential calculus, linear algebra, and statistics.

  • Programming experience not necessary, but some experience with MATLAB (R) is very useful.

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HOW YOU WILL LEARN

WHAT LEARNERS ARE SAYING

MIT XPRO LEARNERS ARE NOT ONLY SCIENTISTS, ENGINEERS, TECHNICIANS, MANAGERS AND CONSULTANTS – THEY ARE CHANGE AGENTS. THEY TAKE THE INITIATIVE, PUSH BOUNDARIES, AND DEFINE THE FUTURE.

Vivian D'Souza

Vivian D'Souza, Model Based Systems Analysis Engineer at Dana Incorporated

The course was a fantastic blend of concepts and practical applications. Professor Youssef's content is unmatched to other similar courses that I've …

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Rachael Naoum

Rachael Naoum, Product Definition Engineer at Dassault Systems

I loved this course. At first, I was a bit intimidated, it's been a while since I've done any hardcore math. However, the layout of this course made …

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Tolga Kaya

Tolga Kaya, Professor of Electrical and Computer Engineering at Sacred Heart University

This course allowed me to dig deeper [into] the foundations of machine learning and the underlying mechanism of the main algorithms that are used. As…

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Bill Bear

Bill Bear, Agile Transformation Coach

Great course for learning the concepts and methods behind machine learning! The course was prepared and delivered in a thoughtful way that provided …

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Juharasha Shaik

Juharasha Shaik, Senior Staff Software Engineer at Visa

This course has helped me gain more understanding of the various algorithms that can be applied to the problems that we face during data analysis and…

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MIT FACULTY AND INSTRUCTORS

Youssef M. Marzouk

Youssef M. Marzouk

Faculty Co-Director of MIT Center of Computational Engineering, Associate Professor of Aeronautics & Astronautics and Director of Aerospace Computational Design Laboratory, MIT

MIT Center for Computational Science and Engineering

MIT Center for Computational Science and Engineering

MIT CCSE Faculty

COURSES IN THIS PROGRAM

To earn a Professional Certificate, you must complete both courses in the program. For those who do not want to commit to the full program, courses can be taken on an individual basis. Savings apply when enrolling into the full program.

Machine Learning, Modeling, and Simulation Principles Machine Learning, Modeling, and Simulation Principles

Course 1 of 2 in the program Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI

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Applying Machine Learning to Engineering and Science Applying Machine Learning to Engineering and Science

Course 2 of 2 in the program Machine Learning, Modeling, and Simulation: Engineering Problem-Solving in the Age of AI

view detail
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News and Events

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