• 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) = $(x,y)$
• Guess for the form of the hypothesis function $h(x;w)$
  • Example : $h(x;w)=w_0+w_1{x}_1+w_2{x}_2$
• For an arbitrary guess for $w$
  • We will get some $\hat{y}=h(x;w)$ $\text{which will not match the ground truth }y$
• Define a cost function $J(y,\hat{y}(w))$ depending on the difference.
• Find optimal $w$ by minimizing $J(w)$ 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: