Machine Learning: Difference between revisions
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The goal is to predict a continuous value. | The goal is to predict a continuous value. | ||
See {{Internal|Regression|Regression}} | See: {{Internal|Regression|Regression}} | ||
===Classification Problem=== | ===Classification Problem=== | ||
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A ''classification problem'' is the problem of identifying which category (out of a set of categories) an example belongs to. The goal of a classification problem is to predict a discrete value out of a set of possible discrete values. | A ''classification problem'' is the problem of identifying which category (out of a set of categories) an example belongs to. The goal of a classification problem is to predict a discrete value out of a set of possible discrete values. | ||
{{ | See: {{Internal|Classification|Classification}} | ||
==Unsupervised Learning== | ==Unsupervised Learning== | ||
Revision as of 21:01, 18 December 2017
Machine Learning
The science of getting computers to learn without being explicitly programmed (Arthur Samuel).
A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E (Tom Mitchell)
Neural Networks
Learning algorithms that mimic how the brain works.
Natural Language Processing (NLP)
Learning Algorithms
Supervised Learning
In supervised learning, we have at our disposal a dataset that tell us which is the "correct answer".
Regression Problem
The goal is to predict a continuous value.
See:
Classification Problem
A classification problem is the problem of identifying which category (out of a set of categories) an example belongs to. The goal of a classification problem is to predict a discrete value out of a set of possible discrete values.
See:
Unsupervised Learning
Reinforcement Learning
Recommender System
Feature
Synonymous with attribute.
- Infinite number of features.