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Updated the About section and added feedback form
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‎docs/index.html

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<li><a href="#about-the-course" id="toc-about-the-course" class="nav-link active" data-scroll-target="#about-the-course">About the Course</a></li>
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<li><a href="#what-youll-learn" id="toc-what-youll-learn" class="nav-link" data-scroll-target="#what-youll-learn">What you’ll learn</a></li>
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<li><a href="#course-instructors" id="toc-course-instructors" class="nav-link" data-scroll-target="#course-instructors">Course Instructors</a></li>
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<li><a href="#course-support-team" id="toc-course-support-team" class="nav-link" data-scroll-target="#course-support-team">Course Support Team</a></li>
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<li><a href="#author" id="toc-author" class="nav-link" data-scroll-target="#author">Author</a>
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<ul class="collapse">
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<li><a href="#co-authors" id="toc-co-authors" class="nav-link" data-scroll-target="#co-authors">Co-Authors</a></li>
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</ul></li>
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<li><a href="#table-of-contents" id="toc-table-of-contents" class="nav-link" data-scroll-target="#table-of-contents">Table of Contents</a></li>
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<li><a href="#study-material" id="toc-study-material" class="nav-link" data-scroll-target="#study-material">Study Material</a></li>
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</div>
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<div class="callout-body-container callout-body">
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<p>This site is still under development.</p>
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<p>Feedback/Correction: <a href="https://forms.gle/LehGfBWFX3yWFDBH6">Click here!</a>.</p>
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</div>
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</div>
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<section id="about-the-course" class="level2">
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<p><strong>Arun Rajkumar</strong></p>
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<p>Assistant Professor, Department of Computer Sciences &amp; Engineering, IIT Madras</p>
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</section>
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<section id="course-support-team" class="level2">
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<h2 class="anchored" data-anchor-id="course-support-team">Course Support Team</h2>
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<section id="author" class="level2">
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<h2 class="anchored" data-anchor-id="author">Author</h2>
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<ul>
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<li>Karthik Thiagarajan (B. Tech., IIT Madras)</li>
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<li>Nitin Kumar Jha (M.Sc., IIT Madras)</li>
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<li><a href="https://discourse.onlinedegree.iitm.ac.in/u/sherry">Sherry</a></li>
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</ul>
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<section id="co-authors" class="level3">
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<h3 class="anchored" data-anchor-id="co-authors">Co-Authors</h3>
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<ul>
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<li><a href="https://discourse.onlinedegree.iitm.ac.in/u/alape_aniruddha">A Aniruddha</a></li>
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<li><a href="https://discourse.onlinedegree.iitm.ac.in/u/vivekthegreat1">Vivek Sivaramakrishnan</a></li>
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</ul>
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</section>
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<section id="table-of-contents" class="level2">
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<h2 class="anchored" data-anchor-id="table-of-contents">Table of Contents</h2>

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‎docs/pages/Wk01.html

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<p>Feedback/Correction: <a href="https://forms.gle/LehGfBWFX3yWFDBH6">Click here!</a>.</p>
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<p>PDF Link: <a href="../notes/Wk01.pdf">Click here!</a></p>
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<section id="introduction-to-machine-learning" class="level1">
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<h1>Introduction to Machine Learning</h1>
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<p>Machine Learning is a sub-field of artificial intelligence concerned with the design of algorithms and statistical models that allow computers to learn from and make predictions or decisions based on data, without being explicitly programmed. It utilizes mathematical optimization, algorithms, and computational models to analyze and understand patterns in data and make predictions about future outcomes.</p>

‎docs/pages/Wk02.html

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<p>PDF Link: <a href="../notes/Wk02.pdf">Click here!</a></p>
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<section id="introduction" class="level1">
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<h1>Introduction</h1>
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<p>In the context of a given dataset <span class="math inline">\(\mathbf{X} \in \mathbb{R}^{d \times n}\)</span>, where <span class="math inline">\(\mathbf{C} \in \mathbb{R}^{d \times d}\)</span> represents the covariance matrix, Principal Component Analysis (PCA) encounters two critical challenges:</p>

‎docs/pages/Wk03.html

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<p>PDF Link: <a href="../notes/Wk03.pdf">Click here!</a></p>
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<section id="introduction-to-clustering" class="level1">
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<h1>Introduction to Clustering</h1>
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<p>Clustering represents an essential method in unsupervised machine learning aimed at grouping similar objects into clusters, thereby revealing inherent structures within the data for exploratory analysis or serving as a preprocessing step for subsequent algorithms.</p>

‎docs/pages/Wk04.html

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<p>PDF Link: <a href="../notes/Wk04.pdf">Click here!</a></p>
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<section id="introduction-to-estimation-in-machine-learning" class="level1">
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<h1>Introduction to Estimation in Machine Learning</h1>
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<p>Estimation in machine learning involves inferring unknown parameters or predicting outcomes from observed data. Estimators, often algorithms or models, are used for these tasks and to characterize the data’s underlying distribution.</p>

‎docs/pages/Wk05.html

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<p>PDF Link: <a href="../notes/Wk05.pdf">Click here!</a></p>
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<section id="introduction-to-supervised-learning" class="level1">
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<h1>Introduction to Supervised Learning</h1>
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<p>Supervised learning, a fundamental machine learning technique, involves training an algorithm on labeled data where the target variable or outcome is known. The primary objective of supervised learning is to establish a mapping between input data and corresponding output variables.</p>

‎docs/pages/Wk06.html

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<p>PDF Link: <a href="../notes/Wk06.pdf">Click here!</a></p>
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<section id="evaluation-of-the-maximum-likelihood-estimator-for-linear-regression" class="level1">
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<h1>Evaluation of the Maximum Likelihood Estimator for Linear Regression</h1>
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‎docs/pages/Wk07.html

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<p>PDF Link: <a href="../notes/Wk07.pdf">Click here!</a></p>
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<section id="introduction-to-binary-classification" class="level1">
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<h1>Introduction to Binary Classification</h1>
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<p>Binary classification is a fundamental task in machine learning, commonly employed in various domains such as computer vision, natural language processing, and bioinformatics. Its objective is to assign objects into one of two categories based on their features.</p>

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