1. Definition:
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.
In general, any machine learning problem can be assigned to one of two broad classifications:
Supervised learning and Unsupervised learning.
What is Machine Learning (Definition of Machine Learning):
Arthur Samuel: The field of study that enables computers to learn without being explicitly programmed.
Tom Mitchell: A program is considered to be able to learn from experience E, solve a task T, and achieve a performance measure P, if and only if, after having experience E, the performance of the program in processing T improves after being judged by P.
Second, supervised learning and unsupervised learning:
1. Supervised Learning - Supervised Learning
Regression: Predict a continuous-valued output based on the known correct results on the training set.
Example: Fitting a graph based on discrete house price coordinates to predict specific house prices in any situation.
Classification problem: Predict the output of a continuous discrete value based on the known correct results on the training set.
Example: According to the characteristics of the tumor (size, shape) to determine the nature of the tumor (benign, malignant).
2. Unsupervised Learning - Unsupervised Learning
Clustering: Divide data into different clusters.
Cocktail Party Problem: Sound Separation, Octave
table plaque:
From: "Statistical Learning Methods" - Li Hang
to be continue
https://www.coursera.org/learn/machine-learning/lecture/db3jS/model-representation