Machine Learning & Data Mining - Prefinals

Artificial Intelligence was coined decades ago in the year 1956 by (John McBeth) at the Dartmouth conference.

(Robot) is a subset of Artificial Intelligence that focuses on getting machines to make decision by feeding them data.

(Supervise Learning) is derived from the word "force labor" which is commonly powered by electricity. It performs task based on its programming.

A series of algorithm which forms a network like brain neutrons is a (artificial neural network).

A weak artificial intelligence is known also as (artificial general intelligence).

(Artificial General Intelligence) is also known as strong AI which involves machines that possess the ability to perform any intellectual task that human can do.

An (artificial intelligence) is a machine made of metal which has programming and perfoms its task based on it.

A machine learning which is based or fed by labels of data inputted by the developer/user withouth learning with its own is (deep learning).

An (regression) is a machine learning algorithm use in supervise learning which categorizes data based on its class.

(Reinforcement Learning) is the combination of reinforcement learning and deep learning which uses series of algorithm and can learn on its own based on the series of decision it creates.

One theoretical foundation of data mining is based on the pattern it analyzes or occuring in the database is (Data compression).

(Voice data mining) makes use of audio/sound signals to indicate the patterns of data or the features of data mining results.

One theoretical foundation of data mining is (Pattern discovery) reduce the data representation which trades accuracy for speed.

(Data compression) foundation of data mining is to compress the given data by encoding in terms of Bits, Clusters, etc.

Theoretical foundation of data mining that is to discover joint probability distributions of random variables is (inductive databases).