ML is not directly programmed to solve a problem,
but..
ML learns from examples & experiences
What makes a 2 from handwriting?
Hard to program?
What distinguish a 2 from a 7?
Collect examples that specify the correct output for a given input
ML algorithm then takes these examples and produces a program that does the job
Some tasks that are useful with learning algorithm
Recognizing patterns:
Digital images and videos:
Recognizing anomalies:
Spam filtering, fraud detection:
Recommendation systems:
Information retrieval:
Many more!
Types of learning tasks
Supervised learning
Unsupervised learning
Supervised Learning
Supervised learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output.
Y = f(X)
GOAL = to approximate the mapping function so well that when you have new input data (x) that you can predict the output variables (Y) for that data.
Supervised Learning
Classification
Supervised Learning (Algorithm)
Regression
Example of Regression
Unsupervised learning is where you only have input data (X) and no corresponding output variables or labeled instances.
GOAL = to model the underlying structure or distribution in the data in order to learn more about the data.
Unsupervised Learning
Unsupervised Learning
Clustering
Unsupervised Learning (Algorithm)
Association
Example of Clustering
Example of Association
Wait..
There is one more learning task..
Problems where you have a large amount of input data (X) and only some of the data is labeled (Y) are called semi-supervised learning problems.
These problems sit in between both supervised and unsupervised learning.
Semi-supervised Learning
Semi-supervised Learning
Semi-supervised Learning (Algorithm)
Keep learning, Keep growing
Machine Learning is all about..