• What is Machine Learning
• Machine Learning Life Cycle
  • Training-Validation-Error
    • Error Train-Test Validation
  • Model Training
    • Gradient Descent
      • Batch Gradient Descent
      • Stochastic Gradient Descent

• Machine Learning is the science (and art) of programming computers so they can learn from data.
• Here is a slightly more general definition:

Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed.

— Arthur Samuel, 1959

• And a more engineering-oriented one:

A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.

— Tom Mitchell, 1997

Here the `rules` is difined as `models`

To learn the parameters, we follow this paradigm

• Collect lots of data pairs (Input Vector, Output Vector) = $\large{\color{Purple} (x, y)}$
• Guess for the form of the hypothesis function $\large{\color{Purple} h(x; w)}$
  • Example : $\large{\color{Purple}h(x; w)= w_0+w_1x_1 + w_2x_2}$
• For an arbitrary guess for $\large{\color{Purple}w}$
  • We will get some $\large{\color{Purple} \hat{y}= h(x; w)}$ ${\color{Blue}\textrm{which will not match the ground truth }} \large {\color{Purple} y}$
• Define a cost function $\large{\color{Purple} J(y, \hat{y}(w))}$ depending on the difference.
• Find optimal $\large{\color{Purple} w}$ by minimizing $\large{\color{Purple} J(w)}$ by using some optimization procedure such as Gradient Descent.

• Machine Learning systems can be classified according to the amount and type of supervision they get during training.
• There are four major categories: