Coursera lecture summary
What is Machine Learning?
Two definitions of Machine Learning are offered.
Arthur Samuel described is as : "the field of study that gives computers the ability to learn without being explicitly programmed." This is an older, indormal definition.
Tom Mitchell prviides 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 programm will win the next game.
In general, any machine learning problem can be assigned to one of two broad calssifications:
Supervised learning and Unsupervised learning.
'Machine Learning' 카테고리의 다른 글
| Supervised Learning & Unsupervised Learning (0) | 2023.03.31 |
|---|---|
| Supervised Learning & Unsupervised Learning (0) | 2023.03.31 |
| [Book][doit_2]분산 학습 (0) | 2023.03.31 |
| NLP 를 위한 CNN (0) | 2023.03.31 |
| Word2Vec (0) | 2023.03.31 |
