dmdw course outcome

8/19/2019 Dmdw Course Outcome

1/8

Meerut Institute of Engineering & Technology, MeerutDepartment of Computer Science & Engineering

Topic covered Periods Tutorial Topics Tut Lab work

Introduction

11

1

2

1

R1 pg no.8

3 Intelligent Agents1

4 1

5 Co mputer vision 1 W1

2

Introduction to Search

! Searching for solutions 11

82

"

3.51

Course / Semester: B.tech 8 th Semester

Subject Name: Artificial Intelligence (ECS-8 !" .

#acult$ %a&e' Shil#a $an%e&

(.%o.

Re)erences

*nit+,

Introduction to Arti cialIntelligence

R1 pg no.3

Intelligence, ArtificialIntelligence,

Applications of AI,History of AIPerformanceMeasure, AgentEnvironment, and

Agent Architecture,Computer Vision

Introductionto PR ! "!anguage #Programming

Foundations andHistory of Arti cialIntelligence,Applications of Arti cialIntelli ence

R1 pgno.31

Structure of Intelligent

Agents

R1 pg

no.35

atural !anguage"ossessing

R3 pgno.22!

$atural !anguageProcessing

*nit+,,

R2- pgno.25

Pro%lem, Pro%lem&pace, Informed and'niformed &earch,(readth) first anddepth *first &earch

Program inprolog toappend listand %u%%lesort+#niformed search

strategiesR3- pgno.1!4

Informed searchstrategies, !ocal searchalgorithms andoptimistic pro$lems,Adversarial Search

R2 pgno.5 Hill Clim%ing,

pro%lems in Hillclim%ing, (est irst&earch, A- and A -

Implementation of .epth


2/8

11.5

Knowledge Representation & Reasoning

11 "ropositional logic1

112

4

132

114 %esolution 1

152

1 1/+0

1! ayesian et'or(s 2

Machine Learning

18 1/+0

1" Decision trees 1

2 1

1

211

222

23 %einforcement learning 1

Pattern Recognition

243

W2

, *pruning

+Search for games,Alpha ) eta pruning

R2 pgno.231

*nit+,,,

R1 pgno.151

Predicate !ogic ,&emantic $et,&tructured analysis

Implementation of (readthfirst search

*heory of rst orderlogic, Inference in Firstorder logic

R1 pgno185

For'ard & ac('ardchaining

R1 pgno.2!2

Inference Rule,Resolution Proofs,Pro%a%ility and(ayes theoremPro%lems

&olvingamily

relationsclauses usingpredicatelogic

R1 pgno.2!!

"ro$a$ilistic reasoning,#tility theory

R2 pgno.1!2

Hidden +ar(ov +odelsH++-HMM(ayesian $et2or3R2 pg

no.1!"

*nit+,/

Supervised andunsupervised learning

R2 pgno.34!

!earning in pro%lem&olving

inding theedges oflength 4 or 5n6in a givengraph+

R1 pgno.531

Statistical learningmodels

R1 pgno.525

Passive learning in7no2n environment,Passive learning inun3no2nenvironment

Implementation of 8ravelsalesmanpro%lem

!earning 'ith completedata ) aive ayesmodels

R1 pgno.588

!earning 'ith hiddendata . E+ algorithm

R1 pgno5"8

*nit+/

Introduction, Designprinciples of patternrecognition system


3/8

25

2

W31

23

W4

1

2!2

W5

'otal No of ectures 45 9

Introduction,approaches toPattern recognition,Component of PRsystem, PCA and!.A .iscussion

Implementation of 8o2erof Hanoipro%lem

Statistical "atternrecognition methods )"rinciple ComponentAnalysis "CA- and!inear Discriminant

Analysis !DA-,Classi cation *echni/ues . earest

eigh$or - %ule,ayes Classi er

(ayes Classifier, 7)$$ rule, $earest$eigh%or rule, 7)mean clusteringPro%lems

Implementation of Mon3ey%ananapro%lem

Support 0ector +achineS0+-, 1 . means

clustering

'e)t boo*s +eferences

+!: Stuart +ussell, $eter Nor ig, Artificial Intelligence A 0o%ern A##roach1, $earsonE%ucation+2: Elaine +ich an% 3e in 3night, Artificial Intelligence1, 0c4ra5-6ill+7: an 9. $atterson, Artificial Intelligence an% E)#ert S&stems1, $rentice 6all of In%ia

9eb references :9! : htt#://555.ecse.r#i.e%u/ ;ji/C/ )franc /tal*s/franc-#rintro 7.##t97: 555.cse.unr.e%u/ bebis/CS?@ / ectures/$CA= A=Case=Stu%ies.##t9 : htt#://555.robots.o).ac.u*/ %claus/%igits/neighbour.htm

9 : htt#://555.%treg.com/s m.htm


4/8

Institute of Engineering D 'echnolog&,EC' +E $ AN

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ata Processing- #or& o) ata Preprocessing >1.41.5

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nitistribution

=verview- :otivation )or ata :ining7- ata :ining+e)inition ? #unctionalities

ata 0leaning' :issing /alues- %ois$ ata- inning-0lustering- Regression7 ,nconsistent ata

ata ,ntegration ?Trans)or&ation ata Reductionata 0ube aggregation

i&ensionalit$ reduction %u&erosit$ Reduction-0lustering- iscreti@ation and 0oncept ierarc $generation

0oncept escription'+ e)inition- ata ;enerali@ation9nal$tical 0 aracteri@ation- 9nal$sis o) attributerelevance

:ining 0lass co&parisons- (tatistical &easures inlarge atabases :easuring 0entral Tendenc$

-asic (tatistical

class escription- :ining 9ssociation Rules in Largeatabases-

9ssociation rule &ining- &ining (ingle+

i&ensional oolean 9ssociation rules )ro&Transactional atabasesA 9priori algorit &

:inin :ultilevel 9ssociation rules )ro& Transaction


5/8

2.8 > >

7 3.1 >

7

3.2 >

3.3 ack propagation 9lgorit &- 0lassi)ication &et ods >

3.4

B+nearest neig bor classi)iers ;enetic 9lgorit &.

>

3.5> >

3.> >

3.!

;rid ased :et ods+ (T,%;- 0L,C*6-

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3.8>

4.1 >

4.2>

4.3

4.4

>4.5

4.>

5.1 9ggregation- Cuer$ #acilit$- ,ntroduction to =L9P >5.2 =L9P )unction and Tools. =L9P (ervers >5.3 R=L9P- :=L9P- >

5.4 ata :ining inter)ace- (ecurit$- ackup and Recover$>5.5

5. Tuning ata Ware ouse- Testing ata Ware ouse > >

atabases and :ining :ulti+i&ensional 9ssociation rules )ro& Relationalatabases

0lassi)ication and Predictions'W at is 0lassi)ication ?Prediction-,ssues regarding 0lassi)ication and prediction-

ecision tree

0luster 9nal$sis' ata t$pes in cluster anal$sis-0ategories o) clustering &et ods- Partitioning&et ods.


6/8

C2#%SE 2#*C2+E3

escr e an ut @e a range o tec n Dues o ata & n ng an ata ware ous ng s$ste&s.!. $ t e end o) t is course t e student s ould be able to'

2 *nderstand t e )unctionalit$ o) t e various data &ining and data ware ousing co&ponents-

7 pprec ate t e strengt s an & tat ons o var ous ata & n ng an ata ware ous ng &o e s.

0o&pare t e various approac es to data &ining and data ware ousing i&ple&entations


7/8

0eerut Institute of Engineering D 'echnolog&, 0eerut

CF +SE E


8/8

8otalE;am

0/ >0

dmdw course outcome

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