There are 3 key essences to help us decide whether to use ML (Machine Learning).
As we explore the Key Essence of ML, let's consider the following questions.
When can Machine Learning?
Why can Machine Learning?
How can Machine Learning?
How can Machine Learning be Better?
When can Machine Learning?
What is Machine Learning?
From Learning to Machine Learning
Learning: acquiring skill with experience accumulated from observations
observations -> learning -> skill
Machine Learning: acquiring skill with experience accumulated/computed from data
data -> ML -> skill
What is skill?
skill -> improve some performance measure (e.g. prediction accuracy)
Machine Learning: improving some performance measure with experience computed from data
data -> ML -> improved performance measure
Why use Machine Learning?
For example: Tree recognition
=> Handwriting 100 rules to identify a tree?
=> Do children learn to identify a tree by memorizing the 100 rules?
=>=> Children understand what a tree is by observing many trees by themselves.
=> In some applications, ML is an alternative route to build a complicated system.
=>=> Let the machine analyze the data by itself and learn to do certain things by itself.
Some Use Scenarios
When human cannot program the system manually
=> navigating on Mars
=>=> We cannot know in advance what the rover will encounter on Mars, and we cannot pre-write a large number of rules so that the rover will adopt a certain rule when it encounters a certain situation.
=>=> ML let the rover on Mars learn how to adapt to the environment on Mars through observation. Let the rover learn how to act on Mars by interacting with the environment.
When human cannot “define the solution” easily
=> speech/visual recognition
When needing rapid decisions that humans cannot do
=> high-frequency trading
When needing to be user-oriented in a massive scale
=> consumer-targeted marketing
=>=> provide personalized service for each user
Key Essence of Machine Learning
Machine Learning: improving some performance measure with experience computed from data
data -> ML -> improved performance measure
Key 1. exists some “underlying pattern” to be learned
so ‘performance measure’ can be improved
Key 2. but no programmable (easy) definition
so ML is needed
Key 3. somehow there is data about the pattern
so ML has some ‘inputs’ to learn from
The three key essences help us decide whether to use ML (Machine Learning).
I hope that this information will help. If you need any further information, please feel free to contact me. https://discord.gg/qHj8sHrctS
