Computer science algorithm.

Computer science algorithm.

1. Machine learn

   • why Used
     • Decisions about data
     • Learn
       • From data
   • Scientists Says:
     • Set of techniques
       • Inside
         • Even more ambitious goal
           • Artificial inteligence
   • What they do conceptualy
     • Clasification
       • Clasify some thing
         • Analyzing
           • Data
       • Algorithm name
         • Clasifier
           • Features
             • Values that usefully
               • Characterize
                 • Things
                   • we whish to clasify
             • Example
               • Moth Clasifier
                 • 2 -Features
                   • Wingspan
                   • Mass
                 • Training Data
                   • Entomologist
                     • Colect Data to process
                       • Luna
                       • Emperor moths
                       • Labeled Data
                         • LAbel data is a Tabel which and especialisty classify the kind of subject he is searching giving them some classification acordingly with its features. Whic is some values sizes or references to comparation.
             • Represent lines in graphs
               • X
                 • WingSpan
                 • Space
               • Y
                 • Mass
                 • Time
             • used to
               • plot Data
                 • overlap
                   • Meaning
                     • To have something in comon
                     • TO ocupy the same area in part
                   • Machine Learn Algorithm
                     • Find Optimal
                       • Seplaratkon
                       • Clasification
                       • Condition
                         • X-axis
                           • X(Wing Span) < 45 mm
                             • Emperor Moth
                         • Y-axis
                           • Y(mass) < .75
                             • Emperor Moth
                               • So It takes both features to consideration and clasify within the group which one is an emperor moth and which one is the emperor moth
           • Training Data
             • Some especialist
               • Colect & Organize
                 • Data
       • BAsicaly compare
         • Images
         • Sound
         • Touch
       • Decision Tree
         • Algorithm forests
           • Contains alot tree algorithm
       • Support vector machine
         • Dry lines in the graph
           • Polynomials
           • other fancy math mathematical function
           • To find the most accurate decision baundaries
         • 3 lines 3D decision baundaries
   • algorithm
     • Job
       • at a high level
         • Maximize
           • Correct classification
             • The algorith compare the data given to it. And try to organize in the most efective way the subejcts and informations given o it.
         • Minimize errors
   • Artificial Neural Network
     • Neurons
       • Cells
         • Process
           • one or more impulses
             • from other cells
         • Transmit
           • Messsges
             • using
               • Signals
                 • Electrical
                 • chemical
           • Their own signal to other cells
     • Artificial Neurons
       • Take a series of inputs
         • Combine them
           • Emits a signal
             • Rather than beig electrical or chemical signals, artificial neurons take numbers in and spit numbers out.
       • Takes data entry
         • Process
           • artifical neral network
             • organize and analyze features
               • Spit out the information
       • First Layer
         • Imput Layer
           • Data needing classification
             • X-axis
               • Wing Span
               • Space
             • Y-axis
               • Mass
               • Time
       • Last layer
         • Output layer
           • Emperor Moth
           • Luna Moth
           • Activation Function or transfer function
             • Perform a final mathematical modifications
               • To result
                 • For example
                   • Limiting value to a range
                     • from -1 to +1
                   • seting any negative values
                     • To zero
       • Classification Decision
         • Hidden Layer
           • transform
             • Input
               • into
                 • Out put
           • Does
             • The hard work of classification
           • Aply the BIAS
             • A fixed value to sum or subtract
             • Random
               • when a neural network is created
                 • an algorithm goes in and starts tweaking
                   • All those values to train the neural network
                     • using
                       • Label data
                         • For training and testing
                           • a process very much like Human learning
       • were invented
         • over fifty years ago

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