update of schedule

2022-11-21 13:46:34 +01:00
parent 5973d2b30f
commit c886416f8d
6 changed files with 30 additions and 20 deletions
@@ -172,14 +172,16 @@ For the reading assignments we use the following abbreviations:

 ###  Week 47 November 21-25
 - Lab Wednesday: Work on project 3
- Lecture Thursday: Support Vector Machines
+- Lecture Thursday: Dimensionality reduction and unsupervised learning: Principal Component analysis (PCA) and clustering 
  - Video of Lecture 
- Lecture Friday:  Support Vector Machines and Summary of course
+- Lecture Friday: PCA and clustering  and Summary of Course
  - Video of Lecture 
 - Reading recommendations:
  - See lecture notes for week 47 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
-  - Geron's chapter 5.
-  - Hastie et al Chapter 12 (sections 1,2.1-12.3 are the most relevant ones)
-  - Bishop chapter 7, with sections 7.1 and 7.2 as the essential ones
- 
- 
+  - Geron's chapter 9 on PCA
+  - Hastie et al Chapter 13 (sections 13.1-13.2 are the most relevant ones)
+- Excellent videos:
+  - We recommend highly the video on PCA by Brunton and Kutz at http://www.databookuw.com/page-2/page-4/, see in particular the video of section 1.5. 
+  - And another good video on PCA is at https://www.youtube.com/watch?v=FgakZw6K1QQ
+  - k-means clustering video at https://www.youtube.com/watch?v=4b5d3muPQmA
+
@@ -805,12 +805,12 @@ const thebe_selector_output = ".output, .cell_output"
 <h3>Week 47 November 21-25<a class="headerlink" href="#week-47-november-21-25" title="Permalink to this headline">¶</a></h3>
 <ul class="simple">
 <li><p>Lab Wednesday: Work on project 3</p></li>
-<li><p>Lecture Thursday: Support Vector Machines</p>
+<li><p>Lecture Thursday: Dimensionality reduction and unsupervised learning: Principal Component analysis (PCA) and clustering</p>
 <ul>
 <li><p>Video of Lecture</p></li>
 </ul>
 </li>
-<li><p>Lecture Friday:  Support Vector Machines and Summary of course</p>
+<li><p>Lecture Friday: PCA and clustering  and Summary of Course</p>
 <ul>
 <li><p>Video of Lecture</p></li>
 </ul>
@@ -818,9 +818,15 @@ const thebe_selector_output = ".output, .cell_output"
 <li><p>Reading recommendations:</p>
 <ul>
 <li><p>See lecture notes for week 47 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>Geron’s chapter 5.</p></li>
-<li><p>Hastie et al Chapter 12 (sections 1,2.1-12.3 are the most relevant ones)</p></li>
-<li><p>Bishop chapter 7, with sections 7.1 and 7.2 as the essential ones</p></li>
+<li><p>Geron’s chapter 9 on PCA</p></li>
+<li><p>Hastie et al Chapter 13 (sections 13.1-13.2 are the most relevant ones)</p></li>
+</ul>
+</li>
+<li><p>Excellent videos:</p>
+<ul>
+<li><p>We recommend highly the video on PCA by Brunton and Kutz at <a class="reference external" href="http://www.databookuw.com/page-2/page-4/">http://www.databookuw.com/page-2/page-4/</a>, see in particular the video of section 1.5.</p></li>
+<li><p>And another good video on PCA is at <a class="reference external" href="https://www.youtube.com/watch?v=FgakZw6K1QQ">https://www.youtube.com/watch?v=FgakZw6K1QQ</a></p></li>
+<li><p>k-means clustering video at <a class="reference external" href="https://www.youtube.com/watch?v=4b5d3muPQmA">https://www.youtube.com/watch?v=4b5d3muPQmA</a></p></li>
 </ul>
 </li>
 </ul>
@@ -172,14 +172,16 @@ For the reading assignments we use the following abbreviations:

 ###  Week 47 November 21-25
 - Lab Wednesday: Work on project 3
- Lecture Thursday: Support Vector Machines
+- Lecture Thursday: Dimensionality reduction and unsupervised learning: Principal Component analysis (PCA) and clustering 
  - Video of Lecture 
- Lecture Friday:  Support Vector Machines and Summary of course
+- Lecture Friday: PCA and clustering  and Summary of Course
  - Video of Lecture 
 - Reading recommendations:
  - See lecture notes for week 47 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
-  - Geron's chapter 5.
-  - Hastie et al Chapter 12 (sections 1,2.1-12.3 are the most relevant ones)
-  - Bishop chapter 7, with sections 7.1 and 7.2 as the essential ones
- 
- 
+  - Geron's chapter 9 on PCA
+  - Hastie et al Chapter 13 (sections 13.1-13.2 are the most relevant ones)
+- Excellent videos:
+  - We recommend highly the video on PCA by Brunton and Kutz at http://www.databookuw.com/page-2/page-4/, see in particular the video of section 1.5. 
+  - And another good video on PCA is at https://www.youtube.com/watch?v=FgakZw6K1QQ
+  - k-means clustering video at https://www.youtube.com/watch?v=4b5d3muPQmA
+