updated schedule with video
This commit is contained in:
Morten Hjorth-Jensen
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# Sphinx build info version 1
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@@ -54,7 +54,7 @@ For the reading assignments we use the following abbreviations:
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- Lecture Thursday: Summary from last week on SVD, more on Statistics, probability theory and linear regression
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- Video of Lecture at https://youtu.be/qn_BAVhMD8U
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- Friday: Linear Regression and more links with Statistics, Resampling methods and presentation of first project.
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- Video of Lecture
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- Video of Lecture at https://youtu.be/_CPGg0JYH8M
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- Reading recommendations:
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- Lectures on Regression for week 36 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
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button.copybtn img {
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div.highlight {
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'copy_to_clipboard': '复制到剪贴板',
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'copy_success': '复制成功!',
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'copy_failure': '复制失败',
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},
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'it' : {
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'copy': 'Copiare',
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'copy_to_clipboard': 'Copiato negli appunti',
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'copy_success': 'Copiato!',
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'copy_failure': 'Errore durante la copia',
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<path d="M5 12l5 5l10 -10" />
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</svg>`
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<title>${messages[locale]['copy_to_clipboard']}</title>
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/**
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* Set up copy/paste for code blocks
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el.innerHTML = iconCheck;
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setTimeout(() => {el.innerHTML = iconCopy}, 2000)
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const clipboardButton = id =>
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`<button class="copybtn o-tooltip--left" data-tooltip="${messages[locale]['copy']}" data-clipboard-target="#${id}">
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${iconCopy}
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<img src="${path_static}copy-button.svg" alt="${messages[locale]['copy_to_clipboard']}">
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</button>`
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codeCell.insertAdjacentHTML('afterend', clipboardButton(id))
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{% endmacro %}
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{% macro head_js_preload() %}
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<title>3. Linear Regression — Applied Data Analysis and Machine Learning</title>
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<link rel="stylesheet"
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<script src="_static/jquery.js"></script>
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<script src="_static/clipboard.min.js"></script>
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<script src="_static/copybutton.js"></script>
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||||
<link rel="index" title="Index" href="genindex.html" />
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||||
<link rel="search" title="Search" href="search.html" />
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||||
<link rel="next" title="4. Ridge and Lasso Regression" href="chapter2.html" />
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||||
<link rel="prev" title="2. Linear Algebra, Handling of Arrays and more Python Features" href="linalg.html" />
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||||
<meta name="viewport" content="width=device-width, initial-scale=1" />
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<meta name="docsearch:language" content="None">
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||||
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||||
<meta name="docsearch:language" content="en" />
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||||
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||||
</head>
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||||
<body data-spy="scroll" data-target="#bd-toc-nav" data-offset="80">
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@@ -97,7 +94,7 @@ const thebe_selector_output = ".output, .cell_output"
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</a>
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||||
</li>
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||||
</ul>
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||||
<p aria-level="2" class="caption" role="heading">
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<p class="caption" role="heading">
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||||
<span class="caption-text">
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About the course
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</span>
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@@ -119,7 +116,7 @@ const thebe_selector_output = ".output, .cell_output"
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</a>
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||||
</li>
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||||
</ul>
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||||
<p aria-level="2" class="caption" role="heading">
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<p class="caption" role="heading">
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<span class="caption-text">
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Review of Statistics with Resampling Techniques and Linear Algebra
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</span>
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@@ -136,7 +133,7 @@ const thebe_selector_output = ".output, .cell_output"
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</a>
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</li>
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</ul>
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<p aria-level="2" class="caption" role="heading">
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<p class="caption" role="heading">
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<span class="caption-text">
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From Regression to Support Vector Machines
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</span>
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@@ -173,7 +170,7 @@ const thebe_selector_output = ".output, .cell_output"
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</a>
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||||
</li>
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||||
</ul>
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<p aria-level="2" class="caption" role="heading">
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<p class="caption" role="heading">
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<span class="caption-text">
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Decision Trees, Ensemble Methods and Boosting
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</span>
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@@ -190,7 +187,7 @@ const thebe_selector_output = ".output, .cell_output"
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</a>
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||||
</li>
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||||
</ul>
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<p aria-level="2" class="caption" role="heading">
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<p class="caption" role="heading">
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<span class="caption-text">
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Dimensionality Reduction
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</span>
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@@ -207,7 +204,7 @@ const thebe_selector_output = ".output, .cell_output"
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</a>
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||||
</li>
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||||
</ul>
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<p aria-level="2" class="caption" role="heading">
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<p class="caption" role="heading">
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<span class="caption-text">
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Deep Learning Methods
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</span>
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||||
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data-placement="left">.ipynb</button></a>
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onClick="window.print()" data-toggle="tooltip" data-placement="left">.pdf</button>
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||||
@@ -302,7 +299,7 @@ const thebe_selector_output = ".output, .cell_output"
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||||
</div>
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||||
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||||
<!-- Table of contents -->
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||||
<div class="d-none d-md-block col-md-2 bd-toc show noprint">
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||||
<div class="d-none d-md-block col-md-2 bd-toc show">
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||||
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||||
<div class="tocsection onthispage pt-5 pb-3">
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<i class="fas fa-list"></i> Contents
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||||
@@ -430,138 +427,7 @@ const thebe_selector_output = ".output, .cell_output"
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||||
</div>
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||||
<div id="main-content" class="row">
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||||
<div class="col-12 col-md-9 pl-md-3 pr-md-0">
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||||
<!-- Table of contents that is only displayed when printing the page -->
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||||
<div id="jb-print-docs-body" class="onlyprint">
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||||
<h1>Linear Regression</h1>
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<!-- Table of contents -->
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||||
<div id="print-main-content">
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||||
<div id="jb-print-toc">
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||||
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||||
<div>
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<h2> Contents </h2>
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</div>
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||||
<nav aria-label="Page">
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||||
<ul class="visible nav section-nav flex-column">
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||||
<li class="toc-h2 nav-item toc-entry">
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||||
<a class="reference internal nav-link" href="#introduction">
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||||
3.1. Introduction
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</a>
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</li>
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||||
<li class="toc-h2 nav-item toc-entry">
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||||
<a class="reference internal nav-link" href="#what-is-machine-learning">
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||||
3.2. What is Machine Learning?
|
||||
</a>
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||||
<ul class="nav section-nav flex-column">
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||||
<li class="toc-h3 nav-item toc-entry">
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||||
<a class="reference internal nav-link" href="#a-frequentist-approach-to-data-analysis">
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||||
3.2.1. A Frequentist approach to data analysis
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||||
</a>
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||||
</li>
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||||
<li class="toc-h3 nav-item toc-entry">
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||||
<a class="reference internal nav-link" href="#what-is-a-good-model">
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3.2.2. What is a good model?
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</a>
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||||
</li>
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||||
</ul>
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</li>
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||||
<li class="toc-h2 nav-item toc-entry">
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||||
<a class="reference internal nav-link" href="#simple-linear-regression-model-using-scikit-learn">
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3.3. Simple linear regression model using
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||||
<strong>
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||||
scikit-learn
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</strong>
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</a>
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||||
<ul class="nav section-nav flex-column">
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||||
<li class="toc-h3 nav-item toc-entry">
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||||
<a class="reference internal nav-link" href="#organizing-our-data">
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||||
3.3.1. Organizing our data
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||||
</a>
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||||
</li>
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||||
</ul>
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||||
</li>
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||||
<li class="toc-h2 nav-item toc-entry">
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||||
<a class="reference internal nav-link" href="#linear-regression-basic-elements">
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||||
3.4. Linear Regression, basic elements
|
||||
</a>
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||||
<ul class="nav section-nav flex-column">
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||||
<li class="toc-h3 nav-item toc-entry">
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||||
<a class="reference internal nav-link" href="#the-chi-2-function">
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3.4.1. The
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<span class="math notranslate nohighlight">
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||||
\(\chi^2\)
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</span>
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||||
function
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||||
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|
||||
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||||
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|
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|
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Review of Statistics with Resampling Techniques and Linear Algebra
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From Regression to Support Vector Machines
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Decision Trees, Ensemble Methods and Boosting
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Dimensionality Reduction
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Deep Learning Methods
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<h1>Building a Feed Forward Neural Network</h1>
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<a class="reference internal nav-link" href="#defining-the-cost-function">
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14.1. Defining the cost function
|
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|
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|
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14.1.1. Example: binary classification problem
|
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|
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|
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14.1.2. The Softmax function
|
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|
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|
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|
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|
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|
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14.2. Developing a code for doing neural networks with back propagation
|
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|
||||
14.2.1. Collect and pre-process data
|
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|
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|
||||
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|
||||
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14.2.2. Train and test datasets
|
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|
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|
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|
||||
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14.2.3. Define model and architecture
|
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|
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|
||||
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14.2.4. Layers
|
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|
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|
||||
<a class="reference internal nav-link" href="#feed-forward-pass">
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14.2.5. Feed-forward pass
|
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|
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14.2.6. Choose cost function and optimizer
|
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14.2.7. Optimizing the cost function
|
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|
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14.2.8. Regularization
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14.2.9. Matrix multiplication
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14.3. Improving performance
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14.4. Evaluate model performance on test data
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|
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14.5. Adjust hyperparameters
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|
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14.6. Visualization
|
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14.7. scikit-learn implementation
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|
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14.8. Visualization
|
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|
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<a class="reference internal nav-link" href="#building-neural-networks-in-tensorflow-and-keras">
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14.9. Building neural networks in Tensorflow and Keras
|
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14.10. The Breast Cancer Data, now with Keras
|
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|
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|
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<a class="reference internal nav-link" href="#fine-tuning-neural-network-hyperparameters">
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14.11. Fine-tuning neural network hyperparameters
|
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|
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14.12. Which activation function should I use?
|
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|
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Dimensionality Reduction
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Review of Statistics with Resampling Techniques and Linear Algebra
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<span class="caption-text">
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From Regression to Support Vector Machines
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Decision Trees, Ensemble Methods and Boosting
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Dimensionality Reduction
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Deep Learning Methods
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<h1>Convolutional Neural Networks</h1>
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16.1. Neural Networks vs CNNs
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16.2. Layers used to build CNNs
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|
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16.3. Mathematics of CNNs
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16.3.1. Convolution Examples: Polynomial multiplication
|
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16.3.2. Convolution Examples: Principle of Superposition and Periodic Forces (Fourier Transforms)
|
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|
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16.4. Two-dimensional Objects
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|
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16.5. More on Dimensionalities
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16.6. Further Dimensionality Remarks
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|
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<a class="reference internal nav-link" href="#cnns-in-more-detail-building-convolutional-neural-networks-in-tensorflow-and-keras">
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16.7. CNNs in more detail, building convolutional neural networks in Tensorflow and Keras
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16.7.1. The MNIST dataset again
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16.7.2. Systematic reduction
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16.8. The CIFAR01 data set
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Review of Statistics with Resampling Techniques and Linear Algebra
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From Regression to Support Vector Machines
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Decision Trees, Ensemble Methods and Boosting
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Dimensionality Reduction
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Deep Learning Methods
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17. Recurrent neural networks: Overarching view
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17.3. Predicting New Points With A Trained Recurrent Neural Network
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17.4. Other Types of Recurrent Neural Networks
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18. Generative Models
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18.1. Generative Adversarial Networks
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18.2. Writing Our First Generative Adversarial Network
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18.2.1. MNIST and GANs
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Review of Statistics with Resampling Techniques and Linear Algebra
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Decision Trees, Ensemble Methods and Boosting
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Dimensionality Reduction
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Deep Learning Methods
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Review of Statistics with Resampling Techniques and Linear Algebra
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From Regression to Support Vector Machines
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Decision Trees, Ensemble Methods and Boosting
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Dimensionality Reduction
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Deep Learning Methods
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5.7. More complicated Example: The Ising model
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5.8.1. Exercise: Ordinary Least Square (OLS) on the Franke function
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5.8.2. Exercise: Bias-variance trade-off and resampling techniques
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5.8.3. Exercise: Cross-validation as resampling techniques, adding more complexity
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5.8.4. Exercise: Ridge Regression on the Franke function with resampling
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5.8.5. Exercise: Lasso Regression on the Franke function with resampling
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Review of Statistics with Resampling Techniques and Linear Algebra
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From Regression to Support Vector Machines
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Decision Trees, Ensemble Methods and Boosting
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Dimensionality Reduction
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||||
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Deep Learning Methods
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||||
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||||
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<h1>Logistic Regression</h1>
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6.1. Logistic Regression
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6.2. Basics
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6.3. The logistic function
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|
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<a class="reference internal nav-link" href="#examples-of-likelihood-functions-used-in-logistic-regression-and-nueral-networks">
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6.4. Examples of likelihood functions used in logistic regression and nueral networks
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Review of Statistics with Resampling Techniques and Linear Algebra
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Decision Trees, Ensemble Methods and Boosting
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Dimensionality Reduction
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Deep Learning Methods
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Review of Statistics with Resampling Techniques and Linear Algebra
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From Regression to Support Vector Machines
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Decision Trees, Ensemble Methods and Boosting
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Dimensionality Reduction
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Deep Learning Methods
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<h1>Decision trees, overarching aims</h1>
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9.1. Basics of a tree
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9.3. Building a tree, regression
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9.3.2. Schematic Regression Procedure
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9.4.1. Visualizing the Tree, Classification
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9.4.2. Other ways of visualizing the trees
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9.5. Algorithms for Setting up Decision Trees
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9.5.2. The CART algorithm for Regression
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9.5.4. Simple Python Code to read in Data and perform Classification
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9.6. Entropy and the ID3 algorithm
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9.6.1. Cancer Data again now with Decision Trees and other Methods
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Review of Statistics with Resampling Techniques and Linear Algebra
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From Regression to Support Vector Machines
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Decision Trees, Ensemble Methods and Boosting
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Dimensionality Reduction
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Deep Learning Methods
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<h1>Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods</h1>
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|
||||
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10.3.1. Making your own Bootstrap: Changing the Level of the Decision Tree
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10.4.1. Compare Bagging on Trees with Random Forests
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10.5. Boosting, a Bird’s Eye View
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10.5.1. Iterative Fitting, Regression and Squared-error Cost Function
|
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10.5.2. Iterative Fitting, Classification and AdaBoost
|
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10.5.3. Adaptive boosting: AdaBoost, Basic Algorithm
|
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10.6. Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent
|
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10.7. Gradient Boosting, Examples of Regression
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10.8. Gradient Boosting, Classification Example
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10.9. XGBoost: Extreme Gradient Boosting
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Review of Statistics with Resampling Techniques and Linear Algebra
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Deep Learning Methods
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||||
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|
||||
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11.3.2. Writing our own PCA code
|
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||||
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||||
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||||
11.3.3. Diagonalize the sample covariance matrix to obtain the principal components
|
||||
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||||
11.4. Classical PCA Theorem
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||||
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11.7.1. Incremental PCA
|
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11.7.3. Kernel PCA
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Review of Statistics with Resampling Techniques and Linear Algebra
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From Regression to Support Vector Machines
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Decision Trees, Ensemble Methods and Boosting
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Dimensionality Reduction
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Deep Learning Methods
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Review of Statistics with Resampling Techniques and Linear Algebra
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From Regression to Support Vector Machines
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Decision Trees, Ensemble Methods and Boosting
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Dimensionality Reduction
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Deep Learning Methods
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<h1>Optimization, the central part of any Machine Learning algortithm</h1>
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About the course
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Review of Statistics with Resampling Techniques and Linear Algebra
|
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From Regression to Support Vector Machines
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||||
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Decision Trees, Ensemble Methods and Boosting
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||||
Dimensionality Reduction
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||||
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Deep Learning Methods
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<h1>Clustering and Unsupervised Learning</h1>
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<span class="caption-text">
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Review of Statistics with Resampling Techniques and Linear Algebra
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<p aria-level="2" class="caption" role="heading">
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<p class="caption" role="heading">
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<span class="caption-text">
|
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From Regression to Support Vector Machines
|
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Decision Trees, Ensemble Methods and Boosting
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Dimensionality Reduction
|
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Deep Learning Methods
|
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Review of Statistics with Resampling Techniques and Linear Algebra
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From Regression to Support Vector Machines
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Dimensionality Reduction
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Deep Learning Methods
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<h1>Applied Data Analysis and Machine Learning</h1>
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Machine learning
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Required Technologies
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Python installers
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Useful Python libraries
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About the course
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Review of Statistics with Resampling Techniques and Linear Algebra
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From Regression to Support Vector Machines
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Decision Trees, Ensemble Methods and Boosting
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Dimensionality Reduction
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Deep Learning Methods
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<h1>Linear Algebra, Handling of Arrays and more Python Features</h1>
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@@ -7,8 +7,8 @@
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>Teaching schedule with links to material — Applied Data Analysis and Machine Learning</title>
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@@ -31,7 +31,7 @@
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<script>var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
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About the course
|
||||
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|
||||
</a>
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<p aria-level="2" class="caption" role="heading">
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<p class="caption" role="heading">
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<span class="caption-text">
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||||
Review of Statistics with Resampling Techniques and Linear Algebra
|
||||
</span>
|
||||
@@ -134,7 +131,7 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</a>
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<p aria-level="2" class="caption" role="heading">
|
||||
<p class="caption" role="heading">
|
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<span class="caption-text">
|
||||
From Regression to Support Vector Machines
|
||||
</span>
|
||||
@@ -171,7 +168,7 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</a>
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<p aria-level="2" class="caption" role="heading">
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||||
<p class="caption" role="heading">
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<span class="caption-text">
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||||
Decision Trees, Ensemble Methods and Boosting
|
||||
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||||
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||||
</a>
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||||
Dimensionality Reduction
|
||||
</span>
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||||
@@ -205,7 +202,7 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</a>
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||||
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<p aria-level="2" class="caption" role="heading">
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<p class="caption" role="heading">
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||||
Deep Learning Methods
|
||||
</span>
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||||
@@ -282,7 +279,7 @@ const thebe_selector_output = ".output, .cell_output"
|
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data-placement="left">.md</button></a>
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<button type="button" id="download-print" class="btn btn-secondary topbarbtn" title="Print to PDF"
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onClick="window.print()" data-toggle="tooltip" data-placement="left">.pdf</button>
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||||
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<i class="fas fa-list"></i> Contents
|
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@@ -392,102 +389,7 @@ const thebe_selector_output = ".output, .cell_output"
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||||
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|
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<h1>Teaching schedule with links to material</h1>
|
||||
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|
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<h2> Contents </h2>
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<nav aria-label="Page">
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<ul class="visible nav section-nav flex-column">
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<li class="toc-h2 nav-item toc-entry">
|
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<a class="reference internal nav-link" href="#weekly-schedule">
|
||||
Weekly Schedule
|
||||
</a>
|
||||
<ul class="nav section-nav flex-column">
|
||||
<li class="toc-h3 nav-item toc-entry">
|
||||
<a class="reference internal nav-link" href="#week-34-august-22-26">
|
||||
Week 34 August 22-26
|
||||
</a>
|
||||
</li>
|
||||
<li class="toc-h3 nav-item toc-entry">
|
||||
<a class="reference internal nav-link" href="#week-35-august-29-september-2">
|
||||
Week 35 August 29-September 2
|
||||
</a>
|
||||
</li>
|
||||
<li class="toc-h3 nav-item toc-entry">
|
||||
<a class="reference internal nav-link" href="#week-36-september-5-9">
|
||||
Week 36 September 5-9
|
||||
</a>
|
||||
</li>
|
||||
<li class="toc-h3 nav-item toc-entry">
|
||||
<a class="reference internal nav-link" href="#week-37-september-12-16">
|
||||
Week 37 September 12-16
|
||||
</a>
|
||||
</li>
|
||||
<li class="toc-h3 nav-item toc-entry">
|
||||
<a class="reference internal nav-link" href="#week-38-september-19-23">
|
||||
Week 38 September 19-23
|
||||
</a>
|
||||
</li>
|
||||
<li class="toc-h3 nav-item toc-entry">
|
||||
<a class="reference internal nav-link" href="#week-39-september-26-30">
|
||||
Week 39 September 26-30
|
||||
</a>
|
||||
</li>
|
||||
<li class="toc-h3 nav-item toc-entry">
|
||||
<a class="reference internal nav-link" href="#week-40-october-3-7">
|
||||
Week 40 October 3-7
|
||||
</a>
|
||||
</li>
|
||||
<li class="toc-h3 nav-item toc-entry">
|
||||
<a class="reference internal nav-link" href="#week-41-october-10-14">
|
||||
Week 41 October 10-14
|
||||
</a>
|
||||
</li>
|
||||
<li class="toc-h3 nav-item toc-entry">
|
||||
<a class="reference internal nav-link" href="#week-42-october-17-21">
|
||||
Week 42 October 17-21
|
||||
</a>
|
||||
</li>
|
||||
<li class="toc-h3 nav-item toc-entry">
|
||||
<a class="reference internal nav-link" href="#week-43-october-24-28">
|
||||
Week 43 October 24-28
|
||||
</a>
|
||||
</li>
|
||||
<li class="toc-h3 nav-item toc-entry">
|
||||
<a class="reference internal nav-link" href="#week-44-october-31-november-4">
|
||||
Week 44 October 31-November 4
|
||||
</a>
|
||||
</li>
|
||||
<li class="toc-h3 nav-item toc-entry">
|
||||
<a class="reference internal nav-link" href="#week-45-november-7-11">
|
||||
Week 45 November 7-11
|
||||
</a>
|
||||
</li>
|
||||
<li class="toc-h3 nav-item toc-entry">
|
||||
<a class="reference internal nav-link" href="#week-46-november-14-18">
|
||||
Week 46 November 14-18
|
||||
</a>
|
||||
</li>
|
||||
<li class="toc-h3 nav-item toc-entry">
|
||||
<a class="reference internal nav-link" href="#week-47-november-21-25">
|
||||
Week 47 November 21-25
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
</li>
|
||||
</ul>
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||||
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||||
</nav>
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||||
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||||
</div>
|
||||
</div>
|
||||
|
||||
|
||||
<div>
|
||||
|
||||
<div class="tex2jax_ignore mathjax_ignore section" id="teaching-schedule-with-links-to-material">
|
||||
@@ -500,7 +402,7 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
<li><p>A selected number of weekly assignments;</p></li>
|
||||
<li><p>The course is part of the CS Master of Science program, but is open to other bachelor and Master of Science students at the University of Oslo;</p></li>
|
||||
<li><p>The course is offered as a FYS-STK4155 (Master of Science level) and a FYS-STK3155 (senior undergraduate) course;</p></li>
|
||||
<li><p>Videos of teaching material are available via the links at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/web/course.html">https://compphysics.github.io/MachineLearning/doc/web/course.html</a>;</p></li>
|
||||
<li><p>Videos of teaching material are available via the links at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/web/course.html">https://compphysics.github.io/MachineLearning/doc/web/course.html</a></p></li>
|
||||
<li><p>Weekly emails with summary of activities will be mailed to all participants;</p></li>
|
||||
</ol>
|
||||
<div class="section" id="weekly-schedule">
|
||||
@@ -556,7 +458,7 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
<li><p>Lecture Thursday: Summary from last week on SVD, more on Statistics, probability theory and linear regression</p></li>
|
||||
<li><p>Video of Lecture at <a class="reference external" href="https://youtu.be/qn_BAVhMD8U">https://youtu.be/qn_BAVhMD8U</a></p></li>
|
||||
<li><p>Friday: Linear Regression and more links with Statistics, Resampling methods and presentation of first project.</p></li>
|
||||
<li><p>Video of Lecture</p></li>
|
||||
<li><p>Video of Lecture at <a class="reference external" href="https://youtu.be/_CPGg0JYH8M">https://youtu.be/_CPGg0JYH8M</a></p></li>
|
||||
<li><p>Reading recommendations:</p>
|
||||
<ul>
|
||||
<li><p>Lectures on Regression for week 36 at <a class="reference external" href="https://compphysics.github.io/MachineLearning/doc/web/course.html">https://compphysics.github.io/MachineLearning/doc/web/course.html</a>.</p></li>
|
||||
@@ -841,42 +743,54 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
|
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</div>
|
||||
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<p class="prev-next-title">Applied Data Analysis and Machine Learning</p>
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By Morten Hjorth-Jensen<br/>
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By Morten Hjorth-Jensen<br/>
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@@ -7,8 +7,8 @@
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<script>var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
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const thebe_selector = ".thebe,.cell"
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const thebe_selector_input = "pre"
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@@ -56,10 +56,7 @@ const thebe_selector_output = ".output, .cell_output"
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</a>
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</li>
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</ul>
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<p aria-level="2" class="caption" role="heading">
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<span class="caption-text">
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About the course
|
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</span>
|
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@@ -121,7 +118,7 @@ const thebe_selector_output = ".output, .cell_output"
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</a>
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<p aria-level="2" class="caption" role="heading">
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<p class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
Review of Statistics with Resampling Techniques and Linear Algebra
|
||||
</span>
|
||||
@@ -138,7 +135,7 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</a>
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</li>
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<p aria-level="2" class="caption" role="heading">
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<p class="caption" role="heading">
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<span class="caption-text">
|
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From Regression to Support Vector Machines
|
||||
</span>
|
||||
@@ -175,7 +172,7 @@ const thebe_selector_output = ".output, .cell_output"
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From Regression to Support Vector Machines
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<h1>Elements of Probability Theory and Statistical Data Analysis</h1>
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|
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@@ -117,7 +114,7 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</a>
|
||||
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|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<p class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
Review of Statistics with Resampling Techniques and Linear Algebra
|
||||
</span>
|
||||
@@ -134,7 +131,7 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<p class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
From Regression to Support Vector Machines
|
||||
</span>
|
||||
@@ -171,7 +168,7 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</a>
|
||||
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|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<p class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
Decision Trees, Ensemble Methods and Boosting
|
||||
</span>
|
||||
@@ -188,7 +185,7 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<p class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
Dimensionality Reduction
|
||||
</span>
|
||||
@@ -205,7 +202,7 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<p class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
Deep Learning Methods
|
||||
</span>
|
||||
@@ -282,7 +279,7 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
data-placement="left">.md</button></a>
|
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<!-- Download PDF via print -->
|
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<button type="button" id="download-print" class="btn btn-secondary topbarbtn" title="Print to PDF"
|
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onclick="printPdf(this)" data-toggle="tooltip" data-placement="left">.pdf</button>
|
||||
onClick="window.print()" data-toggle="tooltip" data-placement="left">.pdf</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -300,7 +297,7 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</div>
|
||||
|
||||
<!-- Table of contents -->
|
||||
<div class="d-none d-md-block col-md-2 bd-toc show noprint">
|
||||
<div class="d-none d-md-block col-md-2 bd-toc show">
|
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|
||||
<div class="tocsection onthispage pt-5 pb-3">
|
||||
<i class="fas fa-list"></i> Contents
|
||||
@@ -330,40 +327,7 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</div>
|
||||
<div id="main-content" class="row">
|
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<div class="col-12 col-md-9 pl-md-3 pr-md-0">
|
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<!-- Table of contents that is only displayed when printing the page -->
|
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<div id="jb-print-docs-body" class="onlyprint">
|
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<h1>Teachers and Grading</h1>
|
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<!-- Table of contents -->
|
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<div id="print-main-content">
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<div id="jb-print-toc">
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<h2> Contents </h2>
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<nav aria-label="Page">
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<li class="toc-h2 nav-item toc-entry">
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<a class="reference internal nav-link" href="#instructor-information">
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Instructor information
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</a>
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</li>
|
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<li class="toc-h2 nav-item toc-entry">
|
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<a class="reference internal nav-link" href="#teaching-assistants-fall-semester-2022">
|
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Teaching Assistants Fall semester 2022
|
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</a>
|
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</li>
|
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<li class="toc-h2 nav-item toc-entry">
|
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<a class="reference internal nav-link" href="#grading">
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Grading
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</ul>
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</nav>
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<div>
|
||||
|
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<div class="tex2jax_ignore mathjax_ignore section" id="teachers-and-grading">
|
||||
@@ -427,42 +391,54 @@ const thebe_selector_output = ".output, .cell_output"
|
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<p class="prev-next-subtitle">next</p>
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<p class="prev-next-title">Textbooks</p>
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<p class="prevnext-label">next</p>
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<p class="prevnext-title">Textbooks</p>
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</footer>
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<p>
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By Morten Hjorth-Jensen<br/>
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<script src="_static/js/index.be7d3bbb2ef33a8344ce.js"></script>
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</body>
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</html>
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@@ -7,8 +7,8 @@
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>Textbooks — Applied Data Analysis and Machine Learning</title>
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<link href="_static/css/theme.css" rel="stylesheet">
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<link href="_static/css/index.ff1ffe594081f20da1ef19478df9384b.css" rel="stylesheet">
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<link href="_static/css/theme.css" rel="stylesheet" />
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<link href="_static/css/index.c5995385ac14fb8791e8eb36b4908be2.css" rel="stylesheet" />
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<link rel="stylesheet"
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@@ -31,7 +31,7 @@
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<link rel="stylesheet" type="text/css" href="_static/panels-main.c949a650a448cc0ae9fd3441c0e17fb0.css" />
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<link rel="stylesheet" type="text/css" href="_static/panels-variables.06eb56fa6e07937060861dad626602ad.css" />
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<link rel="preload" as="script" href="_static/js/index.be7d3bbb2ef33a8344ce.js">
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<script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
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<script src="_static/jquery.js"></script>
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@@ -41,7 +41,7 @@
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<script src="_static/clipboard.min.js"></script>
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<script src="_static/copybutton.js"></script>
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<script>var togglebuttonSelector = '.toggle, .admonition.dropdown, .tag_hide_input div.cell_input, .tag_hide-input div.cell_input, .tag_hide_output div.cell_output, .tag_hide-output div.cell_output, .tag_hide_cell.cell, .tag_hide-cell.cell';</script>
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<script src="_static/sphinx-book-theme.d59cb220de22ca1c485ebbdc042f0030.js"></script>
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<script src="_static/sphinx-book-theme.12a9622fbb08dcb3a2a40b2c02b83a57.js"></script>
|
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<script>const THEBE_JS_URL = "https://unpkg.com/thebe@0.8.2/lib/index.js"
|
||||
const thebe_selector = ".thebe,.cell"
|
||||
const thebe_selector_input = "pre"
|
||||
@@ -53,10 +53,7 @@ const thebe_selector_output = ".output, .cell_output"
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<link rel="next" title="1. Elements of Probability Theory and Statistical Data Analysis" href="statistics.html" />
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<link rel="prev" title="Teachers and Grading" href="teachers.html" />
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<meta name="viewport" content="width=device-width, initial-scale=1" />
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<meta name="docsearch:language" content="None">
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<meta name="docsearch:language" content="en" />
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</head>
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<body data-spy="scroll" data-target="#bd-toc-nav" data-offset="80">
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@@ -95,7 +92,7 @@ const thebe_selector_output = ".output, .cell_output"
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</a>
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</li>
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</ul>
|
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<p aria-level="2" class="caption" role="heading">
|
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<p class="caption" role="heading">
|
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<span class="caption-text">
|
||||
About the course
|
||||
</span>
|
||||
@@ -117,7 +114,7 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</a>
|
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</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<p class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
Review of Statistics with Resampling Techniques and Linear Algebra
|
||||
</span>
|
||||
@@ -134,7 +131,7 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</a>
|
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</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<p class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
From Regression to Support Vector Machines
|
||||
</span>
|
||||
@@ -171,7 +168,7 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<p class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
Decision Trees, Ensemble Methods and Boosting
|
||||
</span>
|
||||
@@ -188,7 +185,7 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<p class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
Dimensionality Reduction
|
||||
</span>
|
||||
@@ -205,7 +202,7 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
||||
<p aria-level="2" class="caption" role="heading">
|
||||
<p class="caption" role="heading">
|
||||
<span class="caption-text">
|
||||
Deep Learning Methods
|
||||
</span>
|
||||
@@ -282,7 +279,7 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
data-placement="left">.md</button></a>
|
||||
<!-- Download PDF via print -->
|
||||
<button type="button" id="download-print" class="btn btn-secondary topbarbtn" title="Print to PDF"
|
||||
onclick="printPdf(this)" data-toggle="tooltip" data-placement="left">.pdf</button>
|
||||
onClick="window.print()" data-toggle="tooltip" data-placement="left">.pdf</button>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -300,7 +297,7 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</div>
|
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|
||||
<!-- Table of contents -->
|
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<div class="d-none d-md-block col-md-2 bd-toc show noprint">
|
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<div class="d-none d-md-block col-md-2 bd-toc show">
|
||||
|
||||
<div class="tocsection onthispage pt-5 pb-3">
|
||||
<i class="fas fa-list"></i> Contents
|
||||
@@ -325,35 +322,7 @@ const thebe_selector_output = ".output, .cell_output"
|
||||
</div>
|
||||
<div id="main-content" class="row">
|
||||
<div class="col-12 col-md-9 pl-md-3 pr-md-0">
|
||||
<!-- Table of contents that is only displayed when printing the page -->
|
||||
<div id="jb-print-docs-body" class="onlyprint">
|
||||
<h1>Textbooks</h1>
|
||||
<!-- Table of contents -->
|
||||
<div id="print-main-content">
|
||||
<div id="jb-print-toc">
|
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|
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<div>
|
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<h2> Contents </h2>
|
||||
</div>
|
||||
<nav aria-label="Page">
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<ul class="visible nav section-nav flex-column">
|
||||
<li class="toc-h1 nav-item toc-entry">
|
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<a class="reference internal nav-link" href="#">
|
||||
Textbooks
|
||||
</a>
|
||||
</li>
|
||||
<li class="toc-h1 nav-item toc-entry">
|
||||
<a class="reference internal nav-link" href="#links-to-relevant-courses-at-the-university-of-oslo">
|
||||
Links to relevant courses at the University of Oslo
|
||||
</a>
|
||||
</li>
|
||||
</ul>
|
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|
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</nav>
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</div>
|
||||
</div>
|
||||
|
||||
|
||||
<div>
|
||||
|
||||
<div class="section" id="textbooks">
|
||||
@@ -420,42 +389,54 @@ The weekly plans will include reading suggestions from these two textbooks.
|
||||
|
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</div>
|
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<p class="prev-next-subtitle">next</p>
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<p class="prev-next-title"><span class="section-number">1. </span>Elements of Probability Theory and Statistical Data Analysis</p>
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<p class="prevnext-label">next</p>
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<p class="prevnext-title"><span class="section-number">1. </span>Elements of Probability Theory and Statistical Data Analysis</p>
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Reference in New Issue
Block a user