Machine Learning: Difference between revisions
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In supervised learning, we have at our disposal a dataset that tell us which is the "correct answer". | In supervised learning, we have at our disposal a dataset that tell us which is the "correct answer". | ||
Training Set. | |||
===Regression Problem=== | ===Regression Problem=== |
Revision as of 22:00, 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".
Training Set.
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
- Clustering problems.
Reinforcement Learning
Recommender System
Feature
Synonymous with attribute.
- Infinite number of features.
Organizatorium
- Support vector machine https://en.wikipedia.org/wiki/Support_vector_machine