Introduction To Machine Learning

 

Introducon to Machine Learning

 

Class Expectations • • • • • •

Mobile phones are kept in silent mode Only ML work, and nothing else. No cross talks and parallel discussions On time every time Hard work is the only shortcut ! Less theory and more practice

 

Key expected outcome • Lear Learn n application of ML algorithms in  • tili"e and demonstrate ML skills in your work •• #lear understanding of $ata %cience &pplication of $ata 'isuali"ation in 

 

#onnected data

Machine learning &ny data )n memory

#LO $

Operational )nteractive reporting $ashboards &d hoc analysis Hadoo p  ransactional  ransactio nal systems #omple+ implementations %preadmar ts %iloed data

* L OL& *nterprise data warehouse

MO()L *

 

/rom data to decisions and actions $escripti ve 0eports1

3hat happened4

$iagnostic

-redictive

-rescriptive

0)nteractive $ashboards1

0Machine Learning1

0ecommendations 2 &utomation1

3hy did it happen4

3hat will happen4

3hat should ) do4

)nsight

 

3hat is learning4 • 5Learning 5Learning denotes changes in a system that ... enable a system to do the same task more e6ciently the ne+t time.7 8Herbert %imon • 5Learning is any process by which a system improves performance from e+perience.7 8Herbert %imon • 5Learning is constructing or modifying representations of what is being e+perienced.7 8ys"ard Michalski • 5Learning is making useful changes in our minds.7 8Marvin Minsky • 5he goal of machine learning is to build computer systems systems that can adapt and learn from their e+perience.7 e+perience.7 om $ietterich $iet terich

 

%o what is Machine Learning 4

)nstead of writing programs we collect lots of e+amples that specify the correct output for a given input. & machine learning algorithm then takes these e+amples and produces a program that does the 9ob.

 

& (it of History • &rthur %amuel :;.

 

1940s Human reasoning / logic first studied as a formal subject within mathematics (Claude Shannon, urt !odel et al"#

19$0s %he &%uring %est' %est' is roosed) a test for true machine intelligence, e*ected to be assed b+ +ear 000# -arious game.la+ing game.la+ing rograms built# 19$ &artmouth conference' coins the hrase &artificial intelligence'#

190s  #2# funding increased increased (mainl+ militar+"# militar+"# 3amous uote) &5ithin &5ithin a generation ### the the roblem of creating 6artificial intelligence6 will substantiall+ be sol7ed#8

 

Turing Test  he 5uring est7 is proposed in ;

 ypically   ypically after after G= eort

)ncidental

/undamental

$esign Drst

(uild Drst

#ost of poor design

#ost overrun

#atastrophic

esearch 2 )nnovation

)n niche areas

$iverse and &pplied

(egins with *nds with Sey components are -rogress can be measured at Snowledge of science 2 math $esign or build Drst4

#urtseyA %andeep a ut

 

Ma9or paradigms of machine learning •

5Learning by memori"ation.7 $ote learning  8 5Learning • *mployed by Drst machine learning systems, in ; • egression

•

8nsuper-ised learning /Clustering 8 nsupervised identiDcation of natural groups in data

•

8 /e /eedback edback :positive or negative reward> $einorcement learning given at the end of a seuence seuence of  of steps

•

3nalogy 8 $etermine correspondence between two dierent representations

•

isco-ery 8 nsupervised, speciDc goal not given

27  

%upervised learning :classiDcation>

 

nsupervised learning :clustering>

 

   ools ools of the trade

 

Leveraging unstructured data

 

$on?t forget the hype curve

 

%ummary -roblem $etection4 • 3hat are you top ;@ challenges that you want data to solve4 • 3hat are you trying to solve4 • 3hat data do you have4 • #an you group it4 T #lustering • #an you categori"e it4 T #lassiDcation #l assiDcation • How much  How many4 T egression • )s it weird4 T &nomaly detection

 

%ummary The Problem to Solve

The Category of Techniques

Covered in this Course

2 want to grou items b+ similarit+# 2 want to find structure (commonalities" in the data

Clustering

.means clustering

2 want to disco7er relationshis between actions or items

 ssociation >ules

riori

2 want to determine the relationshi between the outcome and the inut 7ariables

>egression

;inear >egression ;ogistic >egression

2 want to assign (