ICS3U-Multipage-HTML/intro.html at main · KristopherZ/ICS3U-Multipage-HTML · GitHub

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<!DOCTYPE html>
<html>
<head>
    <link rel="icon" href="image/icon.png">
    <meta name="viewport" content="width=device-width, initial-scale=1">
    <link rel="stylesheet" href="style.css">
    <title>Machine Learning</title>
</head>
<body>
    <a href="index.html">
    <div style="background-color: rgb(16,14,13);position: relative;color: white;">
        <img src="image/title.png" style="height:100px;"><img src="image/title.png" style="height: 100px;transform: scaleX(-1); position: absolute; right: 0%;">
        <div style="position: absolute;top: 50%;left: 50%;transform: translate(-50%, -50%);"><span style="font-size: 50px;"><b>Machine Learning</b></span></div>
    </div>
    </a>
    <!--Nav bar-->
    <div class="navbar">
        <a href="index.html">Home</a>
        <a href="intro.html">Introduction</a>
        <a href="Algorithm.html">Algorithm</a>
        <a href="app.html">Applications</a>
        <span style="float:right;"><a href="Reference.html">Reference</a></span>
    </div>
    <!--Article-->
    <div style="padding:0 30px">
        <h1>Introduction</h1>
        <blockquote cite="https://en.wikipedia.org/wiki/Machine_learning">
        Machine learning (ML) is the study of computer algorithms that improve automatically through experience.
            It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data,
            known as "training data", in order to make predictions or decisions without being explicitly programmed to do so.
             Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision,
              where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.
        </blockquote>
        <h2 id="Distinction">1.Distinction</h2>
        <p>First, I'd like to clarify the difference between machine learning, AI, deep learning, neutral network.</p>
        <img src="image/2.1.png" style="width: 1000px;">
        <p>From there, we know that machine learning is the part of AI that can use statistical methods to enable machines to improve.</p>
        <h2>2.Understanding machine learning</h2>
        From <a href="#Distinction">1.Distinction</a>, we know that machine learning is finding an approach from input to output. More mathematically,
        machine learning is finding a function/map/mapping f(x) through the exsit data. Moreover, the function f(x) most likely is a probability function output [0,1].
        That's the basic idea of machine learning.
        <h2>3.Examplers</h2>
        <p>Now, we will see what machine learning is through examplers, that will help for you understanding.</p>
        <h3>Example1:Best Fit Line</h3>
        <iframe scrolling="no"
        src="https://www.geogebra.org/classic/NQRpPRwg"
        width="1000px"
        height="600px"
        style="border:0px;">
        </iframe>
        <p>Hope you enjoy this example. We've seen this problem a lot of times, including math, physics, etc. However, do you believe that it is a machine learning problem.
        It is, because it fit the criterion. Computer wants to calculate the best fit line through the data point.
        </p>
        <h3>Example2:Machine Learnng with Neutral Network</h3>
        <iframe
        src="https://playground.tensorflow.org/"
        width="1000px"
        height="600px"
        style="border:0px;">
        </iframe>
        <p>This exampler is much more advanced, it shows the neutral network in machine learning. Moreover, tensorflow is the most popular frame of machine learning. But don't worried, we will deconposite all of these, and shows the key idea of machine learning in next section.</p>
    <br>
    <hr>
    <footer>
        <p><em>Author: Kristopher Zhao<br>
            <a href="mailto:350111704@gapps.yrdsb.ca">350111704@gapps.yrdsb.ca</a></em></p>
    </footer>
    </div>
</body>
</html>