Machine Learning - An Introduction
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This series of articles are the study notes of " Machine Learning ", by Prof. Andrew Ng., Stanford University.This article is the notes of week 1, Introduction.
This article contains the definition of Machine Learning, Supervised Learning and Unsupervised Learning.
An Introduction to Machine Learning
1. What is Machine Learning?
Two definitions of Machine Learning are offered. Arthur Samuel described it as: "the field of study that gives computers the ability to learn without being explicitly programmed." This is an older, informal definition.
Tom Mitchell provides a more modern definition: "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."
Example:
playing checkers.
- E = the experience of playing many games of checkers
- T = the task of playing checkers.
- P = the probability that the program will win the next game.
2. Supervised Learning
In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output.
Regression
Classification
In a classification problem, we are instead trying to predict results in a discrete output. In other words, we are trying to map input variables into discrete categories.
Example:
One feature
Two features
Let's see you want to look at medical records and try to predict of a breast cancer as malignant or benign. If someone discovers a breast tumor, a lump in their breast, a malignant tumor is a tumor that is harmful and dangerous and a benign tumor is a tumor that is harmless. So obviously people care a lot about this.
Let's see a collected data set and suppose in your data set you have on your horizontal axis the size of the tumor and on the vertical axis I'm going to plot one or zero, yes or no, whether or not these are examples of tumors we've seen before are malignant which is one or zero if not malignant or benign. So let's say our data set looks like this where we saw a tumor of this size that turned out to be benign or malignant tumors.
So this example I have five examples of benign tumors shown down here, and five examples of malignant tumors shown with a vertical axis value of one. And let's say we have a friend who tragically has a breast tumor, and let's say her breast tumor size is maybe somewhere around this value. The machine learning question is, can you estimate what is the probability, what is the chance that a tumor is malignant versus benign? To introduce a bit more terminology this is an example of a classification problem. The term classification refers to the fact that here we're trying to predict a discrete value output: zero or one, malignant or benign. And it turns out that in classification problems sometimes you can have more than two values for the two possible values for the output. As a concrete example maybe there are three types of breast cancers and so you may try to predict the discrete value of zero, one, two, or three with zero being benign. Benign tumor, so no cancer. And one may mean, type one cancer, like, you have three types of cancer, whatever type one means. And two may mean a second type of cancer, a three may mean a third type of cancer. But this would also be a classification problem.
3. Unsupervised Learning
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