Regularization and Bias Variance

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Compétences que vous apprendrez

Algorithms, Expectation–Maximization (EM) Algorithm, Graphical Model, Markov Random Field


4.6 (292 évaluations)

  • 5 stars
    71,57 %
  • 4 stars
    19,52 %
  • 3 stars
    5,47 %
  • 2 stars
    2,73 %
  • 1 star
    0,68 %


13 févr. 2017

Filled StarFilled StarFilled StarFilled StarFilled Star

Great course! Very informative course videos and challenging yet rewarding programming assignments. Hope that the mentors can be more helpful in timely responding for questions.


29 avr. 2020

Filled StarFilled StarFilled StarFilled StarFilled Star

Great course, especially the programming assignments. Textbook is pretty much necessary for some quizzes, definitely for the final one.

À partir de la leçon

Review of Machine Learning Concepts from Prof. Andrew Ng's Machine Learning Class (Optional)

This module contains some basic concepts from the general framework of machine learning, taken from Professor Andrew Ng's Stanford class offered on Coursera. Many of these concepts are highly relevant to the problems we'll tackle in this course.

Enseigné par

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    Daphne Koller


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