Artificial Intelligence and Data Mining

Lecture 15 - February 8th, 2017

Artificial Intelligence and Data Mining

• Something Fun
  • quickdraw.withgoogle.com/

Social Media Data

• 2 million emails per second
• 500 million tweets on twitter a day
• 500 million new videos viewed to youtube a minute.

Scientific Data

• Data collected and stored at enormous speeds (GB/hour)
  • remote sensors on a satellite

Other Data Sources?

Need for Data Mining

• Human Analysts: Too much work
• Data Mining: automatically discover useful information

Example:

• Predict if an email is spam or not
• Train a model
  • Show the computer a bunch of emails, some of which are spam, some of which are not
  • Tell the computer which words appear in each email
  • Computer learns words that tend to appear with spam emails, or with not-spam emails.
• Test the model
  • Show the computer a new email
  • Check if the computer predicts the right class

Predictive Data Mining or Supervised learning

Learning

We can think of at least three different problems....

Ex. Imagine I'm trying predict wether my neighbour is going to drive into work, so I can ask for a ride.

• Wether..
  • temp.
  • precipitation
  • day
  • clothes

Memorize vs New Data

Temp	Precip	Day	Clothes
25	None	Sat	Casual	Walk
-5	Snow	Mon	Casual	Drive
15	Snow	Mon	Casual	Walk
-5	Snow	Mon	Casual	?

Noisy Data

Temp	Precip	Day	Clothes
25	None	Sat	Casual	Walk
25	None	Sat	Casual	Walk
25	None	Sat	Casual	Drive
25	None	Sat	Casual	Drive
25	None	Sat	Casual	Walk
25	None	Sat	Casual	Walk
25	None	Sat	Casual	Walk
25	None	Sat	Casual	?

Average the data when it's noisy data

How can we build a model?

• Decision Tree
  • Predict by splitting on attribute values
  • ID3
    • Normal procedure: top down in a recursive divide-and-conquer fashion
    • Process Stops

AI Experience (withGoogle)

• Constructing decision trees (ID3)
  • Normalprocedure:topdowninarecursivedivide-and- conquer fashion
    • First: an attribute is selected for the root node and a branch is created for each possible attribute value
    • Then: the instances are split into subsets (one for each branch extending from the node)
    • Finally: the same procedure is repeated for each branch, using only instances that reach the branch
  • Processstopsifallinstanceshavethesameclass

Attribution Selection:

• Heuristic: Choose the attribute that produces the "purest" nodes
• Entropy of Nodes: Lower the entropy, purer the node

Measuring Purity with Entropy:

• Amount of Information
  • Higher -> messier the group
  • Lower -> purer the group

Bonus:

Trump's Twitter:

• iPhone Entries (Staff) vs Android Entries (Trump)