A Seminar Report on Machine Learing

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    A Seminar Report on

    MACHINE TRANSLATION

    In the partial fulllment for the degree of B.Tech.

    Seminar (8CS9)

    JIET School of Engineering & Technology for Girls

    epartment of Comp!ter Science & Engineering

    "#$%'#$%

    G!i*e* +y, '

    S!-mitte* +y, ' Prof. Kamna Agarwal

    Ms Meenakshi Soni Asst. Professor

    IV ear !VIII Semester"

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    .MACHINE LEARNING / Seminar Report

    ACKNOWLEDGEMENT

    It is a matter of great pleasure for me to submit this report on MACHINE LEARNING, as a part

    of curriculum for awar of !ACHEL"R#S IN $ECHN"L"G% &CSE' egree of Ra(asthan

    $echnical )ni*ersit+, ota &Ra(asthan'-

    At this moment of accomplishment, I am presenting m+ wor. with great prie an pleasure, I

    woul li.e to e/press m+ sincere gratitue to all those who helpe me in the successful

    completion of m+ *enture- I woul li.e to than. our PROF.KAMNA AGARWAL for helping

    me in the successful accomplishment of m+ stu+ an for her timel+ an *aluable suggestions-

    His constructi*e criticism has contribute immensel+ to the e*olution of m+ ieas on the sub(ect-

    I am e/ceeingl+ grateful to m+ Head of Department PROF. MAMTA GARG an other

    facult+ members for their inspiration an encouragement- I woul also li.e to than. m+ parents

    an friens for their o*er whelming an whole hearte encouragement an support without

    which this woul not ha*e been successful-

    MEENAKSHI SONI

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    .MACHINE LEARNING / Seminar Report

    JIET SCHOOL OF ENGINEERING TECHNOLOG! FOR GIRLS" JODHP#R

    DEPARTMENT OF COMP#TER SCIENCE ENGINEERING

    CERTIFICATE

    $his is to certif+ that the report entitle $MACHINE LEARNING% has been carrie out

    b+ MEENAKSHI SONI&nderm+ guiance in partial fulfillment of the egree of !achelor of

    $echnolog+ in COMP#TER SCIENCE ENGINEERING of Ra(asthan $echnical

    )ni*ersit+, ota uring the acaemic +ear012030124-

    &5rof- ammna Agarwal'

    Le't&rer E(am)ner

    &5rof- Mamta Garg'

    Head of Department

    CSE

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    .MACHINE LEARNING / Seminar Report

    A*STRACT

    5resent a+ computer applications re6uire the representation of huge amount of comple/

    .nowlege an ata in programs an thus re6uire tremenous amount of wor.- "ur abilit+ to

    coe the computers falls short of the eman for applications- If the computers are enowe with

    the learning abilit+, then our buren of coing the machine is ease &or at least reuce'- $his is

    particularl+ true for e*eloping e/pert s+stems where the 7bottle3nec.7 is to e/tract the e/pert#s

    .nowlege an fee the .nowlege to computers- $he present a+ computer programs in general

    &with the e/ception of some Machine Learning programs' cannot correct their own errors or

    impro*e from past mista.es, or learn to perform a new tas. b+ analog+ to a pre*iousl+ seen tas.-

    In contrast, human beings are capable of all the abo*e- Machine Learning will prouce smarter

    computers capable of all the abo*e intelligent beha*ior-

    $he area of Machine Learning eals with the esign of programs that can learn rules from

    ata, aapt to changes, an impro*e performance with e/perience- In aition to being one of the

    initial reams of Computer Science, Machine Learning has become crucial as computers are

    e/pecte to sol*e increasingl+ comple/ problems an become more integrate into our ail+

    li*es- $his is a har problem, since ma.ing a machine learn from its computational tas.s re6uires

    wor. at se*eral le*els, an comple/ities an ambiguities arise at each of those le*els-

    So, here we stu+ how the Machine learning ta.e place, what are the methos, remeies

    associate, applications, present an future status of machine learning-

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    .MACHINE LEARNING / Seminar Report

    In*e0

    ACN"8LE9GEMEN$

    CER$I:ICA$E

    A!S$RAC$$hapter % Introduction to Machine &earning '

    %.% () MA$)I*+ &+A,*I*-

    $hapter # &earning means /

    #.% T)+ A,$)IT+$T0,+ 12 A &+A,*I*- A-+*T

    $hapter 3 )istor4 of Machine leaning %#

    3.% The *eural Modeling !Self 1rgani5ed S4stem"

    3.# The S4m6olic $oncept Ac7uisition Paradigm3.3 The Modern Knowledge8Intensi9e Paradigm

    $hapter : (ellsprings of Machine &earning %:

    :.% Statistics

    :.# Brain Models

    :.3 Adapti9e $ontrol Theor4

    :.: Ps4chological Models

    :.; Articial Intelligence

    :.' +9olutionar4 Models

    $hapter ; Machine &earning 19er9iew %'

    ;.% The Aim of Machine &earning

    ;.# Machine &earning as a Science

    $hapter ' $lassication of Machine &earning %amples of Machine &earning Pro6lems

    $hapter < 2uture ?irections #' 9isco*er+ of new facts an theories through obser*ation an e/periment- :or

    e/ample, the isco*er+ of ph+sics an chemistr+ laws-

    $he general effect of learning in a s+stem is the impro*ement of the s+stem#s capabilit+

    to sol*e problems- It is har to imagine a s+stem capable of learning cannot impro*e its problem3

    sol*ing performance- A s+stem with learning capabilit+ shoul be able to o self3changing in

    orer to perform better in its future problem3sol*ing-

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    Learning ? Impro*ing performance 5 at tas. $ b+

    ac6uiring .nowlege using self3changing algorithm

    A through e/perience E in an en*ironment for

    tas. $-

    .MACHINE LEARNING / Seminar Report

    8e also note that earn)n0 'annot ta4e pa'e )n )/oat)on< 8e t+picall+ learn something

    &.nowlege ' to perform some tas.s &$', through some e/perience E, an whether we ha*e

    learne well or not will be (uge b+ some performance criteria 5 at the tas. $- :or e/ample, as

    $om Mitchell put it in his ML boo., for the 7chec.ers learning problem7, the tas. $ is to pla+ the

    game of chec.ers, the performance criteria 5 coul be the percentage of games won against

    opponents, an the e/perience E coul be in the form pla+ing practice games with a teacher &or

    self'- :or learning to ta.e place, we o nee a learning algorithm A for self3changing, which

    allows the learner to get e/perience E in the tas. $, an ac6uire .nowlege &thus change the

    learner#s .nowlege set' to impro*e the learner#s performance at tas. $-

    $here are *arious forms of

    impro*ement of a

    s+stem#s problem3sol*ing abilit+

    #uture Directions

    Research in Machine Learning $heor+ is a combination of attac.ing establishe

    funamental 6uestions, an e*eloping new framewor.s for moeling the nees of new machine

    learning applications- 8hile it is impossible to .now where the ne/t brea.throughs will come, a

    few topics one can e/pect the future to hol inclue.1 Conc)usions

    Machine Learning $heor+ is both a funamental theor+ with man+ basic an compelling

    founational 6uestions, an a topic of practical importance that helps to a*ance the state of the

    art in software b+ pro*iing mathematical framewor.s for esigning new machine learning

    algorithms- It is an e/citing time for the fiel, as connections to man+ other areas are being

    isco*ere an e/plore, an as new machine learning applications bring new 6uestions to be

    moele an stuie- It is safe to sa+ that the potential of Machine Learning an its theor+ lie

    be+on the frontiers of our imagination-

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    .MACHINE LEARNING / Seminar Report

    REFERENCES

    @ Alpa+in, E- &011>'-!ntroduction to achine Learning. Massachusetts, )SA< MI$

    5ress-

    @ http