technifyedWeekly list

Massachusetts Institute of Technology

MIT 18.086 Mathematical Methods for Engineers II, Spring '06

via YouTube Video playlist

Overview

This graduate-level course is a continuation of Mathematical Methods for Engineers I (18.085). Topics include numerical methods; initial-value problems; network flows; and optimization.

View the complete course at: http://ocw.mit.edu/18-086S06

License: Creative Commons BY-NC-SA

More information at http://ocw.mit.edu/terms

More courses at http://ocw.mit.edu

Videos in this playlist 29

  1. Lec 1 | MIT 18.086 Mathematical Methods for Engineers II
  2. Lec 2 | MIT 18.086 Mathematical Methods for Engineers II
  3. Lec 3 | MIT 18.086 Mathematical Methods for Engineers II
  4. Lec 4 | MIT 18.086 Mathematical Methods for Engineers II
  5. Lec 5 | MIT 18.086 Mathematical Methods for Engineers II
  6. 6. Wave Profiles, Heat Equation / point source
  7. Lec 7 | MIT 18.086 Mathematical Methods for Engineers II
  8. Lec 8 | MIT 18.086 Mathematical Methods for Engineers II
  9. Lec 9 | MIT 18.086 Mathematical Methods for Engineers II
  10. Lec 10 | MIT 18.086 Mathematical Methods for Engineers II
  11. Lec 11 | MIT 18.086 Mathematical Methods for Engineers II
  12. Lec 12 | MIT 18.086 Mathematical Methods for Engineers II
  13. Lec 13 | MIT 18.086 Mathematical Methods for Engineers II
  14. Lec 14 | MIT 18.086 Mathematical Methods for Engineers II
  15. Lec 15 | MIT 18.086 Mathematical Methods for Engineers II
  16. Lec 16 | MIT 18.086 Mathematical Methods for Engineers II
  17. Lec 17 | MIT 18.086 Mathematical Methods for Engineers II
  18. Lec 18 | MIT 18.086 Mathematical Methods for Engineers II
  19. Lec 19 | MIT 18.086 Mathematical Methods for Engineers II
  20. Lec 20 | MIT 18.086 Mathematical Methods for Engineers II
  21. Lec 21 | MIT 18.086 Mathematical Methods for Engineers II
  22. Lec 22 | MIT 18.086 Mathematical Methods for Engineers II
  23. Lec 23 | MIT 18.086 Mathematical Methods for Engineers II
  24. Lec 24 | MIT 18.086 Mathematical Methods for Engineers II
  25. Lec 25 | MIT 18.086 Mathematical Methods for Engineers II
  26. Lec 26 | MIT 18.086 Mathematical Methods for Engineers II
  27. Lec 27 | MIT 18.086 Mathematical Methods for Engineers II
  28. Lec 28 | MIT 18.086 Mathematical Methods for Engineers II
  29. Lec 29 | MIT 18.086 Mathematical Methods for Engineers II

Advantages and disadvantages

Advantages

  • Free and complete: every lecture is in the playlist, in order.
  • Watch at 1.5x, skip what you know, rewatch what you don't.
  • Taught by Massachusetts Institute of Technology, one of the strongest names in its field.
  • 278,753 views, so help and notes are easy to find.
  • Completely free.
  • Self-paced: start any time.
  • A clear syllabus (29 videos) you can see before you start.
  • Start watching right away, no sign-up.

Disadvantages

  • No certificate, deadlines or graded work.
  • Quality and depth vary: check that the playlist is finished before you start.
  • No certificate.
  • No graded assignments or feedback.

Some points apply to every course of this kind; see how we rank.

Free

  • Free: Watch on YouTube, no account needed.
  • Certificate: None. Code or take notes along to make it stick.

Massachusetts Institute of Technology is in Tier 1: world-leading universities and India's top institutes of our institution ranking (98/100). OpenCourseWare is the gold standard for free university material.

Similar courses

Compare these

Harvard University

Fundamentals of TinyML

4.567 ratings

Focusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML.

  • Free to audit
  • 5 weeks, 2 - 4 hours per week

Harvard University

Introduction to Data Science with Python

4.3174 ratings

Learn the concepts and techniques that make up the foundation of data science and machine learning.

  • Free to audit
  • 8 weeks, 3 - 5 hours per week

Harvard University

CS50's Introduction to Computer Science

An introduction to the intellectual enterprises of computer science and the art of programming.

  • Free to audit
  • 12 weeks, 5 - 14 hours per week

Harvard University

Data Science: Building Machine Learning Models

4.4133 ratings

Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.

  • Free to audit
  • 8 weeks, 2 - 3 hours per week

Harvard University

CS50's Introduction to Programming with Python

An introduction to programming using Python, a popular language for general-purpose programming, data science, web programming, and more.

  • Free to audit
  • 10 weeks, 3 - 6 hours per week

Harvard University

Data Science: R Basics

4.4273 ratings

Build a foundation in R and learn how to wrangle, analyze, and visualize data.

  • Free to audit
  • 8 weeks, 2 - 3 hours per week