Intro to ML

Definition

"The field of study that gives computers the ability to learn without being explicitly programmed." —Arthur Samuel

"A computer program is said to learn from experience E concerning some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E." — Tom Mitchell

Category

Supervised Learning

We have a dataset and already know the correct outputs, having the idea that there is a relationship between the input and the output. Supervised Learning is categorized into regression and classification problems.

• Regression Problem

  Given the historical data, fit a regression model (which is a trend) to predict the continuous output according to its inputs.

• Classification Problem

  Given the historical data, draw a line (or face) that separate the outputs into binary results.

Unsupervised Learning

Having data and having no idea what the result should look like, we can derive structure by clustering the data based on the relationship among input variables.

Model Representation

Elements

• Input
• Hypothesis Function: a function that maps inputs to outputs.
• Cost Function: a function used to measure the accuracy of hypothesis function.
• Optimization Method: a method that used to minimize the sum cost of the feeding dataset (e.g. Gradient Descent)
• Output

5 Elements of ML

Linear Algebra

Multiplication

Matrix vector

matrix-vector.png

Matrix matrix

matrix-matrix.png

Transpose

transpose.png

Inverse

inverse.png

Properties

Not commutative

A∗B ≠ B∗A

Associative

(A∗B)∗C = A∗(B∗C)

Reference

https://www.mathsisfun.com/algebra/matrix-multiplying.html

https://www.coursera.org/learn/machine-learning/resources/JXWWS

https://medium.com/@mahesh.chavan1997/model-representation-in-machine-learning-2caf22e1edf0