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6 Cards in this Set
- Front
- Back
What are some definitions of Machine Learning? |
Classic: Arthur Samuel described it as "the field of study that gives computers the ability to learn without being explicitly programmed." Modern: Tom Michell described it as "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." |
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Give an example of the modern definition of Machine Learning using: E = the experience of playing many games of checkers P = the probability that the program will win a game of checkers T = the task of playing checkers |
Machine learning is defined by a program that with increased experience, E, improves their performance measure, P, at task T then it is said to be "learning." |
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What is supervised learning? |
In supervised learning, we are given a data set with knowledge of the correct output, given some declared input. Supervised learning is classified into two main categories: 1) Regression: map input into a continuous function 2) Classification: map input into a discrete function |
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For the following examples, what type of machine learning problem is it? 1) Given data about the size of a house on the real estate market, try to predict their price. 2) Given data about the size of a house on the real estate market, determine whether the house will sell for more or less than it's asking price. |
1) Price changes continuously based upon the size of the house, so it is a regression type problem of supervised learning. 2) Since the output falls into discrete values of "more than asking price" or "less than asking price" it is a classification problem of supervised learning. |
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What is unsupervised learning? |
Unsupervised learning is the approach to a problem with little or no idea of what our results should look like. |
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Give two ways of describing unsupervised learning. |
1) Clustering: we group data together based upon relationships among the variables in the data Example: group 1000 essays on US economics based on variables such as word count, sentence length, page count etc. 2) Associative Patterns: we store patterns or things that go together. Example: a doctor, over many years of practice, associates certain patient characteristics to a given illness. There is no logical mapping, just a data set of conditions and illnesses that is not exhaustive. |