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Machine Learning
Learning ,[object Object],[object Object],[object Object]
What is Learning ,[object Object],[object Object],[object Object],[object Object],[object Object]
Induction ,[object Object],[object Object]
Why induction is Okay? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Kinds of learning ,[object Object],[object Object],[object Object]
Kinds of learning (cont’d) ,[object Object],[object Object],[object Object]
Kinds of learning (cont’d) ,[object Object],[object Object],[object Object]
Learning a function ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Example problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Memory ,[object Object],Walk Casual Yes Mon Snow 15 Drive Casual Yes Mon Snow -5 Walk Casual No Sat None 25 Clothes Shop Day Precip Temp
Memory ,[object Object],Casual Yes Mon Snow -5 Walk Casual Yes Mon Snow 15 Drive Casual Yes Mon Snow -5 Walk Casual No Sat None 25 Clothes Shop Day Precip Temp
Memory ,[object Object],[object Object],[object Object],Drive Casual Yes Mon Snow -5 Walk Casual Yes Mon Snow 15 Drive Casual Yes Mon Snow -5 Walk Casual No Sat None 25 Clothes Shop Day Precip Temp
Averaging ,[object Object],[object Object],? Casual No Sat None 25 Walk Casual No Sat None 25 Walk Casual No Sat None 25 Walk Casual No Sat None 25 Drive Casual No Sat None 25 Drive Casual No Sat None 25 Walk Casual No Sat None 25 Walk Casual No Sat None 25 Clothes Shop Day Precip Temp
Averaging ,[object Object],[object Object],Walk Casual No Sat None 25 Walk Casual No Sat None 25 Walk Casual No Sat None 25 Walk Casual No Sat None 25 Drive Casual No Sat None 25 Drive Casual No Sat None 25 Walk Casual No Sat None 25 Walk Casual No Sat None 25 Clothes Shop Day Precip Temp
Generalization ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Casual No Mon Rain 24 Drive Casual No Tue None 27 Drive Casual Yes Mon Snow -5 Drive Casual No Sat None 25 Drive Formal No Sat None 20 Drive Casual No Mon None 30 Walk Casual No Wed Rain 10 Walk Casual Yes Sun None 3 Walk Casual Yes Fri None 22 Clothes Shop Day Precip Temp
The red and the black ,[object Object]
What’s the right hypothesis? ,[object Object]
Now, what’s the right hypothesis ,[object Object]
Now, what’s the right hypothesis ,[object Object]
Variety of Learning Methods ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Decision Trees Hypotheses like this are nice because they're relatively easily interpretable by humans.  So, in some cases, we run a learning algorithm on some data and then show the results to experts in the area (astronomers, physicians), and they find that the learning algorithm has found some regularities in their data that are of real interest to them.
Neural Networks ,[object Object],[object Object],[object Object]
Data mining ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
If…then rules ,[object Object],[object Object]
The Weather Problem ,[object Object],If outlook = sunny and humidity = high then play = no If outlook = rainy and windy = true then play = no If outlook = overcast then play = yes If humidity = normal then play = yes If none of the above then play = yes This can be seen as a disjunction of conjuncts.
Machine Learning successes ,[object Object],[object Object],[object Object],[object Object],[object Object]
Supervised Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Supervised Learning: Goal ,[object Object],[object Object],[object Object],[object Object]
Best Hypothesis ,[object Object],[object Object],[object Object],[object Object],[object Object]
Complexity ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Learning Conjuctions ,[object Object],[object Object],[object Object],[object Object],0 1 0 1 1 0 f1 1 0 0 1 0 1 f2 1 0 1 1 1 1 f3 1 1 1 0 1 0 f4 1 0 1 0 1 0 y
Learning Conjunctions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],0 1 0 1 1 0 f1 1 0 0 1 0 1 f2 1 0 1 1 1 1 f3 1 1 1 0 1 0 f4 1 0 1 0 1 0 y
Algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Algorithm ,[object Object],[object Object],[object Object],[object Object]
Algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Simulation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],0 1 0 1 1 0 f1 1 0 0 1 0 1 f2 1 0 1 1 1 1 f3 1 1 1 0 1 0 f4 1 0 1 0 1 0 y
Simulation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],0 1 0 1 1 0 f1 1 0 0 1 0 1 f2 1 0 1 1 1 1 f3 1 1 1 0 1 0 f4 1 0 1 0 1 0 y 0 1 0 1 1 0 f1 1 0 0 1 0 1 f2 1 0 1 1 1 1 f3 1 1 1 0 1 0 f4 1 0 1 0 1 0 y
A harder problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],0 1 0 1 1 0 f1 1 0 0 1 0 1 f2 1 0 1 1 1 1 f3 1 1 1 0 1 0 f4 1 0 1 1 1 0 y
Disjunctive Normal Form (DNF) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],0 1 0 1 1 0 f1 1 0 0 1 0 1 f2 1 0 1 1 1 1 f3 1 1 1 0 1 0 f4 1 0 1 1 1 0 y
Learning DNF ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Algorithm ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Choosing a feature  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Simulation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],0 1 0 1 1 0 f1 1 0 0 1 0 1 f2 1 0 1 1 1 1 f3 1 1 1 0 1 0 f4 1 0 1 1 1 0 y
Simulation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],0 1 0 1 1 0 f1 1 0 0 1 0 1 f2 1 0 1 1 1 1 f3 1 1 1 0 1 0 f4 1 0 1 1 1 0 y

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  • 22. Decision Trees Hypotheses like this are nice because they're relatively easily interpretable by humans. So, in some cases, we run a learning algorithm on some data and then show the results to experts in the area (astronomers, physicians), and they find that the learning algorithm has found some regularities in their data that are of real interest to them.
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