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11. Introduction to Machine Learning



MIT OpenCourseWare

MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: Eric Grimson

In this lecture, Prof. Grimson introduces machine learning and shows examples of supervised learning using feature vectors.

License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

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49 thoughts on “11. Introduction to Machine Learning
  1. seven layers of protocol: application layer…etc,.
    stack, queue, linked list,…etc.
    top-down approach, bottom-up approach, dynamic programming,…etc.
    round robin search

  2. If there any machine capable of …creating it's own Evolution Matrix then that machine has its own self !!! …I guarantee that MIT don't understand Evolution Matrix code !!

  3. Since 2016 long time no see! E learning, youtube learning googling learning my bookmarks my informations my ideas my approach my life hacks my diy my inspirational informations

  4. I can listen to this teacher 100 hours continuously. the way he spread words and his character.
    Oh this handsome gentleman taught me Python, thank you MIT for such a great content!

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