Machine Learning: Difference between revisions
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Definition: given a training set, learn a function h: X -> Y so that h(x) is a "good" predictor for the corresponding value of y. For historical reason, this function is called hypothesis. The learning algorithm outputs a function (h - hypothesis). The hypothesis function maps input variables to output variables. When the target variable we're trying to predict is continuous, the learning problem is a [[#Regression_Problem|regression problem]]. When the target variable can only take a number of discrete values, we call the problem a [[#Classification_Problem|classification problem]]. | Definition: given a training set, learn a function h: X -> Y so that h(x) is a "good" predictor for the corresponding value of y. For historical reason, this function is called hypothesis. The learning algorithm outputs a function (h - hypothesis). The hypothesis function maps input variables to output variables. When the target variable we're trying to predict is continuous, the learning problem is a [[#Regression_Problem|regression problem]]. When the target variable can only take a number of discrete values, we call the problem a [[#Classification_Problem|classification problem]]. | ||
Cost function: Squared error function (common used one for regression functions). | |||
===Regression Problem=== | ===Regression Problem=== |
Revision as of 22:25, 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".
Input variables, output (target) variables.
A pair of one input variable and one output variable is called a training example.
The dataset is called a training set.
Definition: given a training set, learn a function h: X -> Y so that h(x) is a "good" predictor for the corresponding value of y. For historical reason, this function is called hypothesis. The learning algorithm outputs a function (h - hypothesis). The hypothesis function maps input variables to output variables. When the target variable we're trying to predict is continuous, the learning problem is a regression problem. When the target variable can only take a number of discrete values, we call the problem a classification problem.
Cost function: Squared error function (common used one for regression functions).
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