10 Questions
Who coined the term 'machine learning' and when?
The term machine learning was coined in 1959 by Arthur Samuel, an IBM employee and pioneer in the field of computer gaming and artificial intelligence.
What are the mathematical foundations of machine learning?
The mathematical foundations of ML are provided by mathematical optimization (mathematical programming) methods.
What is machine learning (ML)?
Machine learning is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can effectively generalize and thus perform tasks without explicit instructions.
What are some areas where machine learning approaches have been applied?
Machine learning approaches have been applied to large language models, computer vision, speech recognition, email filtering, agriculture, and medicine, where it is too costly to develop algorithms to perform the needed tasks.
What is the synonym used for 'machine learning' in the time period when the term was coined?
The synonym 'self-teaching computers' was also used in this time period.
Match the following terms with their descriptions in statistics:
Classification = Identifying which category an observation belongs to Explanatory variables = Quantifiable properties of individual observations Classifier = Algorithm that implements classification Logistic regression = Procedure often used for classification in statistics
Match the following types of properties with their examples in statistics:
Categorical = Blood type (e.g. 'A', 'B', 'AB', 'O') Ordinal = Size (e.g. 'large', 'medium', 'small') Integer-valued = Number of occurrences of a word in an email Real-valued = Measurement of blood pressure
Match the following terms with their meanings in statistics:
Distance function = Compares observations based on similarity Explanatory variables = Properties of observations in statistics Classifier = Implements classification in statistics Logistic regression = Procedure often used for classification in statistics
Match the following terms with their usage in statistics:
Classification algorithm = Mathematical function that maps input data to a category Explanatory variables = Properties of observations in statistics Classifier = Algorithm that implements classification Logistic regression = Procedure often used for classification in statistics
Match the following terms with their primary usage in statistics:
Explanatory variables = Quantifiable properties of individual observations Categorical = Blood type (e.g. 'A', 'B', 'AB', 'O') Ordinal = Size (e.g. 'large', 'medium', 'small') Real-valued = Measurement of blood pressure
Test your knowledge of machine learning with this quiz! Explore topics such as artificial neural networks, statistical algorithms, and the application of machine learning to natural language processing.
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