update with links

Author: mhjensen

doc/pub/week13/html/week13-bs.html

@@ -319,9 +319,9 @@ <h2 id="plans-for-the-week-of-april-21-25" class="anchor">Plans for the week of
 <ol>
  <li> Introduction to QML</li>
  <li> Support Vector Machines (SVM, classical machine learning approach)</li>
- <li> Quantum Support Vector Machine Learning (QSVM)
-<!-- o <a href="https://youtu.be/" target="_self">Video of lecture TBA</a> -->
-<!-- o <a href="https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2024/NotesApril17.pdf" target="_self">Whiteboard notes</a> --></li>
+ <li> Quantum Support Vector Machine Learning (QSVM)</li>
+ <li> Video of lecture at <a href="https://youtu.be/C36Kg4eaO7A" target="_self"><tt>https://youtu.be/C36Kg4eaO7A</tt></a></li>
+ <li> Whiteboard notes at <a href="https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesApril23.pdf" target="_self"><tt>https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesApril23.pdf</tt></a></li>  
 </ol>
 </div>
 </div>
@@ -349,8 +349,8 @@ <h2 id="what-is-machine-learning" class="anchor">What is Machine Learning? </h2>
 <ol>
 <li> \textbf{Supervised Learning:} Labeled data for classification or regression.</li>
 <li> \textbf{Unsupervised Learning:} No labels; discover hidden patterns.</li>
+<li> \textbf{Reinforcement Learning:} Learning through interaction with the environment.</li>
 </ol>
-<p>o\textbf{Reinforcement Learning:} Learning through interaction with the environment.</p>
 </div>
 </div>
 

doc/pub/week13/html/week13-reveal.html

@@ -200,9 +200,9 @@ <h2 id="plans-for-the-week-of-april-21-25">Plans for the week of April 21-25 </h
 <ol>
  <p><li> Introduction to QML</li>
  <p><li> Support Vector Machines (SVM, classical machine learning approach)</li>
- <p><li> Quantum Support Vector Machine Learning (QSVM)
-<!-- o <a href="https://youtu.be/" target="_blank">Video of lecture TBA</a> -->
-<!-- o <a href="https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2024/NotesApril17.pdf" target="_blank">Whiteboard notes</a> --></li>
+ <p><li> Quantum Support Vector Machine Learning (QSVM)</li>
+ <p><li> Video of lecture at <a href="https://youtu.be/C36Kg4eaO7A" target="_blank"><tt>https://youtu.be/C36Kg4eaO7A</tt></a></li>
+ <p><li> Whiteboard notes at <a href="https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesApril23.pdf" target="_blank"><tt>https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesApril23.pdf</tt></a></li>  
 </ol>
 </div>
 
@@ -228,9 +228,8 @@ <h2 id="what-is-machine-learning">What is Machine Learning? </h2>
 <ol>
 <p><li> \textbf{Supervised Learning:} Labeled data for classification or regression.</li>
 <p><li> \textbf{Unsupervised Learning:} No labels; discover hidden patterns.</li>
+<p><li> \textbf{Reinforcement Learning:} Learning through interaction with the environment.</li>
 </ol>
-<p>
-<p>o\textbf{Reinforcement Learning:} Learning through interaction with the environment.</p>
 </div>
 
 

doc/pub/week13/html/week13-solarized.html

@@ -252,9 +252,9 @@ <h2 id="plans-for-the-week-of-april-21-25">Plans for the week of April 21-25 </h
 <ol>
  <li> Introduction to QML</li>
  <li> Support Vector Machines (SVM, classical machine learning approach)</li>
- <li> Quantum Support Vector Machine Learning (QSVM)
-<!-- o <a href="https://youtu.be/" target="_blank">Video of lecture TBA</a> -->
-<!-- o <a href="https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2024/NotesApril17.pdf" target="_blank">Whiteboard notes</a> --></li>
+ <li> Quantum Support Vector Machine Learning (QSVM)</li>
+ <li> Video of lecture at <a href="https://youtu.be/C36Kg4eaO7A" target="_blank"><tt>https://youtu.be/C36Kg4eaO7A</tt></a></li>
+ <li> Whiteboard notes at <a href="https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesApril23.pdf" target="_blank"><tt>https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesApril23.pdf</tt></a></li>  
 </ol>
 </div>
 
@@ -280,8 +280,8 @@ <h2 id="what-is-machine-learning">What is Machine Learning? </h2>
 <ol>
 <li> \textbf{Supervised Learning:} Labeled data for classification or regression.</li>
 <li> \textbf{Unsupervised Learning:} No labels; discover hidden patterns.</li>
+<li> \textbf{Reinforcement Learning:} Learning through interaction with the environment.</li>
 </ol>
-<p>o\textbf{Reinforcement Learning:} Learning through interaction with the environment.</p>
 </div>
 
 

doc/pub/week13/html/week13.html

@@ -329,9 +329,9 @@ <h2 id="plans-for-the-week-of-april-21-25">Plans for the week of April 21-25 </h
 <ol>
  <li> Introduction to QML</li>
  <li> Support Vector Machines (SVM, classical machine learning approach)</li>
- <li> Quantum Support Vector Machine Learning (QSVM)
-<!-- o <a href="https://youtu.be/" target="_blank">Video of lecture TBA</a> -->
-<!-- o <a href="https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2024/NotesApril17.pdf" target="_blank">Whiteboard notes</a> --></li>
+ <li> Quantum Support Vector Machine Learning (QSVM)</li>
+ <li> Video of lecture at <a href="https://youtu.be/C36Kg4eaO7A" target="_blank"><tt>https://youtu.be/C36Kg4eaO7A</tt></a></li>
+ <li> Whiteboard notes at <a href="https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesApril23.pdf" target="_blank"><tt>https://github.com/CompPhysics/QuantumComputingMachineLearning/blob/gh-pages/doc/HandWrittenNotes/2025/NotesApril23.pdf</tt></a></li>  
 </ol>
 </div>
 
@@ -357,8 +357,8 @@ <h2 id="what-is-machine-learning">What is Machine Learning? </h2>
 <ol>
 <li> \textbf{Supervised Learning:} Labeled data for classification or regression.</li>
 <li> \textbf{Unsupervised Learning:} No labels; discover hidden patterns.</li>
+<li> \textbf{Reinforcement Learning:} Learning through interaction with the environment.</li>
 </ol>
-<p>o\textbf{Reinforcement Learning:} Learning through interaction with the environment.</p>
 </div>