Download pptx - Computer Science and Engineering

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MATLAB is a multi-paradigm numerical computing environment
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fourth-generation programming language.
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A proprietary programming language developed by MathWorks
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In 2004, MATLAB had around one million users across industry
and academia.
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Cleve Moler, the chairman of the computer science department
at the University of New Mexico, started developing MATLAB in
the late 1970s
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Why?
 It has very attractive visualizations
 It wants to be your friend:
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Very easy to learn
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Great HELP menu and tutorial
 Very useful:
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From a simple calculator to complex data analysis toolbox.
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Many implemented algorithms.
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We all have experimental data that we need to quantify, analyze, and visualize (in a certain
way – for a paper etc…)
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We usually need to repeat our experiments and analysis more than one time
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MATLAB (MATrix LABoratory)
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high-performance language for technical computing
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computation, visualization, and programming in an easy-to-use environment
Typical uses include:
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Math and computation
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Algorithm development
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Modelling, simulation, and prototyping
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Data analysis, exploration, and visualization
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Scientific and engineering graphics
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Application development, including Graphical User Interface building
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A good choice for vision program development because:
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Easy to do very rapid prototyping
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Quick to learn, and good documentation
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A good library of image processing functions
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Excellent display capabilities
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Widely used for teaching and research in universities and industry
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Another language to impress your boss with !
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MATLAB consists of:
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The MATLAB language
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The MATLAB working environment
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a high-level matrix/array language with control flow statements, functions, data structures,
input/output, and object-oriented programming features.
the set of tools and facilities that you work with as the MATLAB user or programmer,
including tools for developing, managing, debugging, and profiling
Handle Graphics
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the MATLAB graphics system. It includes high-level commands for two-dimensional and
three-dimensional data visualization, image processing, animation, and presentation
graphics.
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The MATLAB function library.
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a vast collection of computational algorithms ranging from
elementary functions like sum, sine, cosine, and complex arithmetic,
to more sophisticated functions like matrix inverse, matrix
eigenvalues, Bessel functions, and fast Fourier transforms as well
as special image processing related functions
The MATLAB Application Program Interface (API)
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a library that allows you to write C and Fortran programs that
interact with MATLAB. It include facilities for calling routines from
MATLAB (dynamic linking), calling MATLAB as a computational
engine, and for reading and writing MAT-files.
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Some facts for a first impression
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Everything in MATLAB is a matrix !
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MATLAB is an interpreted language, no compilation needed (but
possible)
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MATLAB does not need any variable declarations, no dimension
statements, has no packaging, no storage allocation, no pointers
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Programs can be run step by step, with full access to all variables,
functions etc.
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Remember:
Another simple example:
t = 0:pi/100:2*pi;
y = sin(t);
plot(t,y)
EVERYTHING IN MATLAB
IS A MATRIX !
creates 1 x 200 Matrix
Argument and result: 1 x 200 Matrix
A simple example:
a=1
while length(a) < 10
a = [0 a] + [a 0]
end
which prints out Pascal’s triangle:
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121
1331
14641
1 5 10 10 5 1
1 6 15 20 15 6 1
1 7 21 35 35 21 7 1
1 8 28 56 70 56 28 8 1
1 9 36 84 126 126 84 36 9 1
(with “a=” before each line).
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Another simple example:
t = 0:pi/100:2*pi;
y = sin(t);
plot(t,y)
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Building matrices with [ ]:
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A = [2 7 4]
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A = [2; 7; 4]
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A = [2 7 4; 3 8 9]
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Don’t have to declare type
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Don’t even have to initialise
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Just assign in command window
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The workspace is Matlab’s memory
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Can manipulate variables stored in the workspace
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Display contents of workspace
>> whos
Name
Size
Bytes Class
a
1x1
8 double array
b
1x1
8 double array
c
1x1
8 double array
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Delete variable(s) from workspace
>> clear all; % delete all variables from workspace
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A'
% transpose
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B*A % matrix multiplication
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B.*A % element by element multiplication
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B/A
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B./A % element by element division
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[B A] % Join matrices (horizontally)
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[B; A]% Join matrices (vertically)
% matrix division
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MATLAB® makes it easy to analyze and visualize your big data so you can improve the design,
performance, and reliability of your products. MATLAB is:
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Easy :
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Convenient :
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Work with the big data storage systems you already use. MATLAB works with your existing systems.
Access big data stored in traditional file systems, SQL and NoSQL databases, and Hadoop/HDFS.
Scalable :
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Don’t know big data programming? Use familiar MATLAB functions and syntax to work with big datasets,
even if they don’t fit in memory.
Use the processing platform that suits your needs. Because MATLAB works with your existing systems,
you can optimally process your algorithms – without having to rewrite them – using anything from your
local desktop machine to Hadoop and Spark.
http://www.mathworks.com/solutions/big-data-matlab/index.html
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A tree is a hierarchical data structure where every node has exactly one parent (expect
the root) and no or several children.
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Along with this relational structure,
each node can store any kind of data.
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This class implements it using plain MATLAB
syntax and arrays. Most useful methods are
implemented, using overloading of MATLAB
functions for tree objects.
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http://tinevez.github.io/matlab-tree/index.html
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This page serves as a basic documentation or tutorial for the @tree class.
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This tutorial is split into several sections, normally independent. It is a good idea
however to read them in order:
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Introduction to trees, the tree class, and basic information.
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Creating, modifying and accessing a tree.
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MATLAB operators for trees.
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Special trees and methods.
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Searching a tree.
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Tree traversal.
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Plotting a tree.
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Engineers and data scientists work with large amounts of data in a variety of formats
such as sensor, image, video, telemetry, databases, and more.
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They use machine learning to find patterns in data and to build models that predict
future outcomes based on historical data.
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With MATLAB®, you have immediate access to prebuilt functions, extensive toolboxes,
and specialized apps for classification, regression, and clustering. You can:
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Compare approaches such as logistic regression, classification trees, support vector
machines, ensemble methods, and deep learning.
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Use model refinement and reduction techniques to create an accurate model that best
captures the predictive power of your data.
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Integrate machine learning models into enterprise systems, clusters, and clouds, and
target models to real-time embedded hardware.
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https://www.mathworks.com/solutions/machine-learning/examples.html?s_iid=solew_trial_mlr_cta1
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