# Machine Learning

AI and Robotics

Oct 17, 2013 (4 years and 8 months ago)

136 views

Machine Learning

Hilary Hamlin

Jacob Curtis

H
istory

Arthur Samuel (1959)

o
"Field of study that gives computers the ability to
learn without being explicitly programmed"

Tom
M.Mitchell

(1998)

o
"A computer program is said to learn from
experience E with respect to some class of tasks T
and performance measure P, if its performance at
tasks in T, as measured by P, improves with
experience E"

Introduction

Learning Machines = Programs that learn

Create Knowledge Bases

o
Symbolic

o
Analog

Learn Concepts

o
Supervised

o
Unsupervised

Supervised Learning

Labeled Training Data

o
pairs of input & output

Generalizes

Determine Labels for new Info

INPUT

SYSTEM

OUTPUT

Training Examples

ƒ

{ (x1, f(x1)), (x2, f(x2))...(
xn,f
(
xn
)) }

for some unknown function (system) y = f(x)

ƒFind f(x)

ƒPredict y'=f(x')

OUTPUT

INFERRED FUNCTION

NEW DATA

INPUT

ID3

BATGIRL

Checkers

world's first self
-
learning program

limited amount of available computer
memory

o
alpha
-
beta pruning

o
rote learning

alpha
-
beta pruning

A

B

C

D

E

F

G

H

I

J

K

MAX

MIN

( 3 )

(12)

( 5)

( 3 )

( 9)

( 2 )

( 7 )

<=3

>=3

<=5

3

<=3

<=2

Min
-
Max O(
b^m
)

Alpha
-
Beta O(b^(m/2))

Checkers: Pruning Tree

Unsupervised Learning

N
o
training data set

N
o
clear 'right' output

Finds
patterns in
data and labeling accordingly

Commonly implemented using clustering

Clustering

John Snow’s Cholera Map

Clustering vs Hierarchical Clustering

Clustering Hierarchical Clustering

E

E1

E2

E3

E4

E7

E8

E

E1 E2

E7 E8

Agglomerative Clustering

1) Find Euclidean Distance between every node

2) Let X and Y be the nodes Closest to each other

3) Cluster X and Y store the distance

4) Repeat until every element is in one cluster

Time complexity θ(n^3)

Better Clustering than θ(n^3)?

Pre
-
process data

Canopy Clustering

Clusters faster

Improves Clusters

Faster Algorithms

BIRCH
(balanced iterative reducing and clustering using hierarchies)

CLARANS

(A Clustering Algorithm based on Randomized Search)

Real World Applications

Artificial “Brain”

Deep Learning Algorithm

Identifies Cats based

Questions?

References

St. Clair, Daniel C. "Learning Programs: Teaching Computers to Acquire
Knowledge."
IEEE Potentials
. October (1992): 19
-
22. Print.

http://www.cse.chalmers.se/edu/year/2011/course/TDA231/

-
s11/papers/samuel_1959_B.pdf

http://www.werc.tu
-
-
tutorial
-
1
-
2.pdf

http://www.cs.princeton.edu/~schapire/talks/picasso
-
minicourse.pdf

http://www.cs.iastate.edu/~cs472/lectures/lecture05
-
game
-
2up.pdf

References (cont)

http://www.guardian.co.uk/news/datablog/2013/mar/15/john
-
snow
-
cholera
-
map

http://www.guardian.co.uk/news/datablog/interactive/2013/mar/15/cholera
-
map
-
john
-
snow
-
recreated

http://geoawesomeness.com/?p=3761