Learning From Data
‘Process’ of Learning
“A process of filtering and transforming data into valid and useful knowledge.”
‘Goal’ of Learning:
“Final goal is to improve the qualities of communication and decision making”
Top-down approach:
“Start with a hypothesis derived from observation or prior knowledge”
Bottom-up approach:
° No hypothesis to test
° Unknown Patterns
° Key relationships
Data Visualization:
“Converting and exploring data into some meaningful data visually is known as Data visualization.”
Artificial Neural Network as Learning Model:
It is modeled after human brain’s network and Simulate biological information processing via networks of interconnected neurons. Neural networks are analog and parallel
Supervised Learning:
In supervised learning, the model defines the effect one set of observations, called inputs, has on another set of observations, called outputs. In other words, the inputs are assumed to be at the beginning and outputs at the end of the causal chain. The models can include mediating variables between the inputs and outputs.
Un-Supervised Learning:
In unsupervised learning, all the observations are assumed to be caused by latent variables, that is, the observations are assumed to be at the end of the causal chain. In practice, models for supervised learning often leave the probability for inputs undefined. This model is not needed as long as the inputs are available, but if some of the input values are missing, it is not possible to infer anything about the outputs. If the inputs are also modeled, then missing inputs cause no problem since they can be considered latent variables as in unsupervised learning.
Maheen Asif
