Update documentation

_sources/chapters/Week7/eig.ipynb

@@ -9,7 +9,7 @@
     "In the previous section, we looked at how to construct the covariance matrix, **C**. Recall that **C** is defined as follows:\n",
     "\n",
     "$$\n",
-    "\\mathbf{C} = \\frac{1}{M-1} \\mathbf{X^TX}\n",
+    "\\mathbf{C} = \\frac{1}{M-1} \\mathbf{X^T X}\n",
     "$$\n",
     "\n",
     "where **X** is size [*M x N*] and **C** is size [*N x N*].\n",

chapters/Week7/eig.html

@@ -435,7 +435,7 @@ <h1>Eigenanalysis<a class="headerlink" href="#eigenanalysis" title="Permalink to
 <p>In the previous section, we looked at how to construct the covariance matrix, <strong>C</strong>. Recall that <strong>C</strong> is defined as follows:</p>
 <div class="math notranslate nohighlight">
 \[
-\mathbf{C} = \frac{1}{M-1} \mathbf{X^TX}
+\mathbf{C} = \frac{1}{M-1} \mathbf{X^T X}
 \]</div>
 <p>where <strong>X</strong> is size [<em>M x N</em>] and <strong>C</strong> is size [<em>N x N</em>].</p>
 <p>Principal Component Analysis (PCA) consists of an <strong>eigenanalysis</strong> of C. So, now we need to familiarize ourselves with eigenanalysis, the process of finding the eigenvalues and eigenvectors of a matrix.</p>
@@ -749,7 +749,7 @@ <h2>Interpretation:<a class="headerlink" href="#interpretation" title="Permalink
 </div>
 </details>
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-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.lines.Line2D at 0x112289030&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.lines.Line2D at 0x113864f70&gt;
 </pre></div>
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 <img alt="../../_images/569725df8aa999373c5554fa215f7efdf65f318819e74f8317e95319190e6f6d.png" src="../../_images/569725df8aa999373c5554fa215f7efdf65f318819e74f8317e95319190e6f6d.png" />
@@ -954,7 +954,7 @@ <h2>A few properties of eigenvectors and eigenvalues:<a class="headerlink" href=
 </div>
 </details>
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-<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.lines.Line2D at 0x11237e3b0&gt;
+<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>&lt;matplotlib.lines.Line2D at 0x1139e23b0&gt;
 </pre></div>
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 <img alt="../../_images/de6ed938a164ee32b6f853d027cd7011bedeb9351f5f87651c59199d6d6d943b.png" src="../../_images/de6ed938a164ee32b6f853d027cd7011bedeb9351f5f87651c59199d6d6d943b.png" />