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stimata-debug/machine-learning.md

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Created November 2, 2016 12:50
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  1. @jaceklaskowski jaceklaskowski revised this gist Aug 21, 2015. 1 changed file with 14 additions and 1 deletion.
    15 changes: 14 additions & 1 deletion machine-learning.md
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    @@ -23,4 +23,17 @@ Learning using https://www.coursera.org/learn/machine-learning/home/welcome
    * Clustering algorithm
    * Cocktail party algorithm
    * The Hypothesis Function
    * measure the accuracy of our hypothesis function by using a cost function.
    * measure the accuracy of our hypothesis function by using a cost function.
    * training examples

    ## Gradient Descent (multiple variables)

    * an *iterative* algorithm of linear regression
    * minimize the cost function `$J(\theta)$`
    * **partial derivative** - what's that? Why is this important for the algorithm?
    * `$\alpha$` - learning rate
    * feature scaling = converge quicker

    ## Normal Equation

    * Works well for smaller set of features, e.g. `n < 10^5`
  2. @jaceklaskowski jaceklaskowski revised this gist Aug 11, 2015. 1 changed file with 16 additions and 2 deletions.
    18 changes: 16 additions & 2 deletions machine-learning.md
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    How much of machine learning is statistics and vice versa?

    Learning using https://www.coursera.org/learn/machine-learning/home/welcome

    * **machine learning** = *teaching a computer to learn concepts using data — without being explicitly programmed.*
    * **Supervised learning** = "right answers" given
    * **Regression problem**
    * continuous valued output
    * decude the function for a given data set and predict other values
    * deduce the function for a given data set and predict other values
    * *"in *regression problems*, we are taking input variables and trying to map the output onto a *continuous* expected result function."*
    * *Univariate linear regression is used when you want to predict a single output value from a single input value.*
    * From https://en.wikipedia.org/wiki/Regression: *Regression analysis is a statistical technique for estimating the relationships among variables.*
    * **Classification problem**
    * discrete valued output
    * From https://en.wikipedia.org/wiki/Statistical_classification: *the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.*
    * Feature = attribute
    * used to predict others
    * used to predict others
    * **Unsupervised learning** = all data is without labels or have the same label
    * Unstructured data
    * Can you find some structure in the dataset?
    * An Unsupervised Learning algorithm can group data into partitions called **clusters**
    * *We're not telling the algorithms about structure of the data set or right answers/examples* = *we know nothing in advance*
    * Clustering algorithm
    * Cocktail party algorithm
    * The Hypothesis Function
    * measure the accuracy of our hypothesis function by using a cost function.
  3. @jaceklaskowski jaceklaskowski revised this gist Aug 11, 2015. 1 changed file with 11 additions and 2 deletions.
    13 changes: 11 additions & 2 deletions machine-learning.md
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    @@ -1,3 +1,12 @@
    How much of machine learning is statistics and vice versa?

    * **Supervised learning** = "right answers" given
    * **Regression problem** = decude the function for a given data set and predict other values
    * From https://en.wikipedia.org/wiki/Regression: *Regression analysis is a statistical technique for estimating the relationships among variables.*
    * **Regression problem**
    * continuous valued output
    * decude the function for a given data set and predict other values
    * From https://en.wikipedia.org/wiki/Regression: *Regression analysis is a statistical technique for estimating the relationships among variables.*
    * **Classification problem**
    * discrete valued output
    * From https://en.wikipedia.org/wiki/Statistical_classification: *the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.*
    * Feature = attribute
    * used to predict others
  4. @jaceklaskowski jaceklaskowski created this gist Aug 11, 2015.
    3 changes: 3 additions & 0 deletions machine-learning.md
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    Original file line number Diff line number Diff line change
    @@ -0,0 +1,3 @@
    * **Supervised learning** = "right answers" given
    * **Regression problem** = decude the function for a given data set and predict other values
    * From https://en.wikipedia.org/wiki/Regression: *Regression analysis is a statistical technique for estimating the relationships among variables.*