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2 changes: 1 addition & 1 deletion _quarto.yml
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title: "ADA511 [0.2]{.small .grey} <br>Foundations of <br>data science and data-driven engineering"
title: "ADA511 [0.2]{.small .grey} <br>AI prototyping<br>inference & decision"
author:
- "S. Mæland [![ORCID](orcid_32x32.png){width=1em}](https://orcid.org/0000-0002-4652-4753)"
- "P.G.L. Porta Mana [![ORCID](orcid_32x32.png){width=1em}](https://orcid.org/0000-0002-6070-0784) [{{< fa house >}}](https://portamana.org/)"
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<meta name="dcterms.date" content="2024-09-28">

<title>ADA511 0.2 Foundations of data science and data-driven engineering - 3&nbsp; Basic decision problems</title>
<title>ADA511 0.2 AI prototypinginference &amp; decision - 3&nbsp; Basic decision problems</title>
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<title>ADA511 0.2 Foundations of data science and data-driven engineering - 27&nbsp; Beyond machine learning</title>
<title>ADA511 0.2 AI prototypinginference &amp; decision - 27&nbsp; Beyond machine learning</title>
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<title>ADA511 0.2 Foundations of data science and data-driven engineering - 33&nbsp; Code design</title>
<title>ADA511 0.2 AI prototypinginference &amp; decision - 33&nbsp; Code design</title>
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4 changes: 2 additions & 2 deletions docs/conditional_probability.html
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<title>ADA511 0.2 Foundations of data science and data-driven engineering - 17&nbsp; Conditional probability and learning</title>
<title>ADA511 0.2 AI prototypinginference &amp; decision - 17&nbsp; Conditional probability and learning</title>
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<title>ADA511 0.2 Foundations of data science and data-driven engineering - Learning and conditional probability: a summary</title>
<title>ADA511 0.2 AI prototypinginference &amp; decision - Learning and conditional probability: a summary</title>
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<title>ADA511 0.2 Foundations of data science and data-driven engineering - 4&nbsp; First connection with machine learning</title>
<title>ADA511 0.2 AI prototypinginference &amp; decision - 4&nbsp; First connection with machine learning</title>
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<title>ADA511 0.2 Foundations of data science and data-driven engineering - 11&nbsp; Second connection with machine learning</title>
<title>ADA511 0.2 AI prototypinginference &amp; decision - 11&nbsp; Second connection with machine learning</title>
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<title>ADA511 0.2 Foundations of data science and data-driven engineering - 19&nbsp; Third connection with machine learning</title>
<title>ADA511 0.2 AI prototypinginference &amp; decision - 19&nbsp; Third connection with machine learning</title>
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<meta name="dcterms.date" content="2023-10-14">

<title>ADA511 0.2 Foundations of data science and data-driven engineering - 25&nbsp; Decision trees</title>
<title>ADA511 0.2 AI prototypinginference &amp; decision - 25&nbsp; Decision trees</title>
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<title>ADA511 0.2 Foundations of data science and data-driven engineering - 9&nbsp; Shortcut rules</title>
<title>ADA511 0.2 AI prototypinginference &amp; decision - 9&nbsp; Shortcut rules</title>
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<title>ADA511 0.2 Foundations of data science and data-driven engineering - 32&nbsp; The Dirichlet-mixture belief distribution</title>
<title>ADA511 0.2 AI prototypinginference &amp; decision - 32&nbsp; The Dirichlet-mixture belief distribution</title>
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<title>ADA511 0.2 Foundations of data science and data-driven engineering - 35&nbsp; Example application: adult-income task</title>
<title>ADA511 0.2 AI prototypinginference &amp; decision - 35&nbsp; Example application: adult-income task</title>
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<title>ADA511 0.2 Foundations of data science and data-driven engineering - 38&nbsp; The prototype Optimal Predictor Machine makes decisions</title>
<title>ADA511 0.2 AI prototypinginference &amp; decision - 38&nbsp; The prototype Optimal Predictor Machine makes decisions</title>
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<title>ADA511 0.2 Foundations of data science and data-driven engineering - 28&nbsp; Exchangeable beliefs</title>
<title>ADA511 0.2 AI prototypinginference &amp; decision - 28&nbsp; Exchangeable beliefs</title>
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<title>ADA511 0.2 Foundations of data science and data-driven engineering - 2&nbsp; Framework</title>
<title>ADA511 0.2 AI prototypinginference &amp; decision - 2&nbsp; Framework</title>
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<meta name="author" content="S. Mæland ORCID">
<meta name="author" content="P.G.L. Porta Mana ORCID ">
<meta name="dcterms.date" content="2024-09-29">
<meta name="dcterms.date" content="2024-10-02">

<title>ADA511 0.2 Foundations of data science and data-driven engineering</title>
<title>ADA511 0.2 AI prototypinginference &amp; decision</title>
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<h1 class="quarto-secondary-nav-title">ADA511 <span class="small grey">0.2</span> <br>Foundations of <br>data science and data-driven engineering</h1>
<h1 class="quarto-secondary-nav-title">ADA511 <span class="small grey">0.2</span> <br>AI prototyping<br>inference &amp; decision</h1>
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<a href="./">ADA511 <span class="small grey">0.2</span><br><span class="small grey">2024-09-29</span><br></a><a href="http://creativecommons.org/licenses/by-sa/4.0"><img src="cc_by_sa.png" class="img-fluid" style="width:3em" alt="CC BY-SA 4.0"> <span class="small grey">licence</span></a><br>
<a href="./">ADA511 <span class="small grey">0.2</span><br><span class="small grey">2024-10-02</span><br></a><a href="http://creativecommons.org/licenses/by-sa/4.0"><img src="cc_by_sa.png" class="img-fluid" style="width:3em" alt="CC BY-SA 4.0"> <span class="small grey">licence</span></a><br>
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<h2 id="toc-title">Table of contents</h2>

<ul>
<li><a href="#dear-student-and-aspiring-data-engineer" id="toc-dear-student-and-aspiring-data-engineer" class="nav-link active" data-scroll-target="#dear-student-and-aspiring-data-engineer">Dear student<br> and aspiring data engineer</a>
<li><a href="#dear-student-and-aspiring-ai-data-engineer" id="toc-dear-student-and-aspiring-ai-data-engineer" class="nav-link active" data-scroll-target="#dear-student-and-aspiring-ai-data-engineer">Dear student<br> and aspiring AI &amp; data engineer</a>
<ul class="collapse">
<li><a href="#your-role-in-the-course-bugs-features" id="toc-your-role-in-the-course-bugs-features" class="nav-link" data-scroll-target="#your-role-in-the-course-bugs-features">Your role in the course <br>Bugs &amp; features</a></li>
<li><a href="#course-structure" id="toc-course-structure" class="nav-link" data-scroll-target="#course-structure">Course structure</a></li>
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<header id="title-block-header" class="quarto-title-block default">
<div class="quarto-title">
<h1 class="title d-none d-lg-block">ADA511 <span class="small grey">0.2</span> <br>Foundations of <br>data science and data-driven engineering</h1>
<h1 class="title d-none d-lg-block">ADA511 <span class="small grey">0.2</span> <br>AI prototyping<br>inference &amp; decision</h1>
</div>


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<div>
<div class="quarto-title-meta-heading">Published</div>
<div class="quarto-title-meta-contents">
<p class="date">2024-09-29</p>
<p class="date">2024-10-02</p>
</div>
</div>

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</header>


<p><img src="ada511logo.png" title="ADA511 [0.2]{.small .grey} <br>Foundations of <br>data science and data-driven engineering" class="quarto-cover-image img-fluid"></p>
<p><img src="ada511logo.png" title="ADA511 [0.2]{.small .grey} <br>AI prototyping<br>inference &amp; decision" class="quarto-cover-image img-fluid"></p>
<div class="hidden">
<p><span class="math display">\[
\DeclarePairedDelimiters{\set}{\{}{\}}
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<p><em>If you can’t join ’em,<br>
beat ’em.</em><br>
<span style="text-align:right;font-size:75%">(<a href="https://www.nobelprize.org/prizes/physics/1965/schwinger/biographical/">J. Schwinger</a>)</span></p>
<section id="dear-student-and-aspiring-data-engineer" class="level1 unnumbered page-columns page-full">
<h1 class="unnumbered">Dear student<br> and aspiring data engineer</h1>
<section id="dear-student-and-aspiring-ai-data-engineer" class="level1 unnumbered page-columns page-full">
<h1 class="unnumbered">Dear student<br> and aspiring AI &amp; data engineer</h1>
<p>The goal of this course is not to help you learn how to tune the parameters of the latest kind of deep network, or how to efficiently handle large amounts of data, or how to do cross-validation in the fastest way, or what is the latest improvement in random-forest algorithms.</p>
<p>The goal of this course is to help you learn the principles to build the machine-learning algorithms and AI devices <em>of the future</em>. And, as a side effect, you’ll also learn how to concretely improve present-day algorithms, and also how to determine if any of them has already reached its maximal theoretical performance.</p>
<p>How can such a goal be achieved?</p>
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