吴恩达机器学习笔记 (一）–初识机器学习

学习基于：吴恩达机器学习.

1. What is Machine Learning?

Two definations 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.”
  (它是一种使计算机无需显式编程就能学习的研究领域。)

• 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, its performance at tasks in T, as measured by P, improves with experience E.”
  （它是一种计算机程序，从经验E中学习关于某些任务T进行性能测量P，任务T的效果根据测量值P随着经验E提高。）

  • E.g.：
    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 problem:

In a regression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function.

• E.g:
  Given data about the size of houses on the real estate market, try to predict their price. Price as a function of size is a continuous output.
  

Classification problem

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.

• E.g.:
  Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.
  

3. Unsupervised learning

Unsupervised learning allows us to approach problems with little or no idea what our results should look like.

• We can derive structure from data where we don’t necessarily know the effect of the variables.
• We can derive this structure by clustering the data based on relationships among the variables in the data.
• With unsupervised learning there is no feedback based on the prediction results.
  • E.g.:
    Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups that are somehow similar or related by different variables, such as lifespan, location, roles, and so on. (Clustering)