2. Machine Learning
• “A breakthrough in machine learning would be worth ten Microsofts” (Bill
Gates, Microsoft)
• “Machine learning is the next Internet”
(Tony Tether, Former Director, DARPA)
• “Web rankings today are mostly a matter of machine learning”
(Prabhakar Raghavan, Dir. Research, Yahoo)
4. What is Machine Learning?
Methods that can help generalize information
from the observed data so that it can be used
to make better decisions in the future
5. Credit Approval
Let’suse a conceptual example to crystallize the issues.
• Using salary, debt, years in residence, etc., approve for credit or not.
• No magic credit approval formula.
• Banks have lots of data.
–customer information: salary, debt, etc.
–whether or not they defaulted on their credit.
age 32 years
gender male
salary 40,000
debt 26,000
years in job 1 year
years at home 3 years
. . . . . .
Approve for credit?
A pattern exists. We don’t know it. We have data to learn
it.
6. What is Machine Learning?
The term Machine Learning is used to characterize a number of different
approaches for generalizing from observed data:
• Supervised learning
-Given a set of features and labels learn a model that will predict a label to a
new feature set
• Unsupervised learning
- Discover patterns in data
8. What is ANN?
An artificial neural network is a crude way of trying to simulate
the human brain (digitally)
• Human brain – Approx. 10 billion neurons
• Each neuron connected with thousands of others
• Parts of neuron
– Cell body
– Dendrites – receive input signal
– Axons – Give output
9. ANN?
ANN – made up of artificial neurons
– Digitally modeled biological neuron
• Each input into the neuron has its own weight associated
with it
• As each input enters the nucleus it's multiplied by its
weight.
