December 2, 2021

Learn some machine learning fundamentals in an afternoon

Here is a plan to learn ML fundamentals in an afternoon by watching some videos on youtube:

Follow this plan

Machine learning fundamentals:

[Stop and drink coffee, eat a snack]

How to address bias and variance:

Extra material:

Test your knowledge

  • What is bias?
    • A: Bla
    • B: The inability of a machine learning model (e.g. linear regression) to express the true relationship between X and Y
    • C: Bla
  • What is variance?
    • A: The difference in how well a model fits different datasets (e.g. training and test)
    • B: Bla
    • C: Bla
  • What problem does regularization, bagging and boosting address?
    • A: Bla
    • B: Bla
    • C: Finds the sweet spot between simple and complicated models
  • What is regularization?
    • A: Bla
    • B: Bla
    • C: Bla
  • What is bagging?
    • A: Bla
    • B: Bla
    • C: Bla
  • What is boosting?
    • A: Bla
    • B: Bla
    • C: Bla
    • What is bootstrapping?
    • A: Repeat an experiment a bunch of times until we feel certain about the result
    • B: Repeatly random sample n times (with replacement) from a set of n observations and build up a histogram of any statistic, e.g. the mean.
    • C: Augment a small set of observations with synthetic samples to increase sample size

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