Introduction to Machine Learning
What is Machine Learning
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 task in T as measured by P, improves with experience E .
— Tom Mitchell (1998)
ML in Knowledge-based System
Automatic knowledge-acquisition component in KBS
By gathering knowledge from experience, this approach avoids the need for knowledge engineer to formally specify all of the knowledge that the computer needs.
Why We Need Machine Learning
Some projects don’t require to actually understand the data since the data is so big.
Better Algorithm
Learning algorithm more effective and efficient
More Data
Larger storage and internet of things
More Processing power
Higher computing power
Learning Types
Supervised Learning
Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples.
Unsupervised Learning
Unsupervised learning is a type of machine learning that utilizes a data set with no pre-existing labels with a minimum of human supervision, often for the purpose of searching for previously undetected patterns.
Reinforcement Learning
Reinforcement learning is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward.
Task Types
Classification
Clustering
Regression
