move section time data to google sheet

add previous course pages to resources
harvard-ml-courses · Jan 22, 2024 · b270578 · b270578
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diff --git a/README.md b/README.md
@@ -4,6 +4,8 @@ Course Website for CS 181: Machine Learning at Harvard University in Spring 2024
 
 Based on design by Alvin Wan (alvinwan.com) for CS294-112 at UC Berkeley.
 
+(Note to staff: See the staff repository for website update instructions.)
+
 Built with [Jekyll](https://jekyllrb.com/docs/) and deployed via [GitHub Pages](https://pages.github.com/) at <https://harvard-ml-courses.github.io/cs181-web/>.
 
 See the Jekyll docs linked above for local development instructions. If you're on a Mac, do *not* use the system installation of Ruby (`/usr/bin/ruby`). Instead, install it via Homebrew (`brew install ruby`) and make sure the path to the new installation is in your `PATH` environment variable.

diff --git a/_data/all.yaml b/_data/all.yaml
@@ -14,52 +14,3 @@ tas:
   - name: <a href="mailto:[email protected]">Derek Zheng</a>
   - name: <a href="mailto:[email protected]">Sam Jones</a>
   - name: <a href="mailto:[email protected]">Neeyanth Kopparapu</a>
-
-ohs:
-  - time: Tuesdays 8-10pm
-    location: Leverett D-Hall
-    tfs: Ahmad and Angela
-  - time: Tuesdays 11am-1pm
-    location: SEC (Room TBD)
-    tfs: Audrey, Derek, and Jeffrey
-  - time: Wednesdays 8:30-10:30pm
-    location: Winthrop D-Hall
-    tfs: Brian and Andrew
-  - time: Thursdays 11am-1pm
-    location: SEC (Room TBD)
-    tfs: Charu and Neeyanth
-  - time: Thursdays 8-10pm
-    location: Currier (Room TBD)
-    tfs: Gabe and Sam
-  - time: Fridays 3-5pm
-    location: Science Center (Room TBD)
-    tfs: Alex and Josh
-  - time: Thursdays 10pm-12am
-    location: Zoom
-    tfs: Shirley and Alyssa
-
-sections:
-  - time: Tuesdays 1-2:15
-    location: SEC (Room TBD)
-    tfs: Andrew and Sam
-  - time: Tuesdays 2:15-3:30
-    location: SEC (Room TBD)
-    tfs: Alyssa and Derek
-  - time: Tuesdays 3:45-5:00
-    location: SEC (Room TBD)
-    tfs: Charu and Neeyanth
-  - time: Wednesdays 2:15-3:30
-    location: Science Center (Room TBD)
-    tfs: Ahmad and Angela
-  - time: Wednesdays 3:45-5:00
-    location: Science Center (Room TBD)
-    tfs: Brian and Shirley
-  - time: Thursdays 2:15-3:30
-    location: SEC (Room TBD)
-    tfs: Alex and Josh
-  - time: Thursdays 3:45-5:00
-    location: SEC (Room TBD)
-    tfs: Gabe and Audrey
-  - time: Fridays 3:45-5:00
-    location: Science Center (Room TBD)
-    tfs: Jeffrey
diff --git a/index.html b/index.html
@@ -17,34 +17,39 @@ <h1 class="head-title">CS 181: Machine Learning (2024)</h1>
 	<div class="main">
 		<h2 class="title">About</h2>
 		<p>
-			CS 181 provides a broad and rigorous introduction to machine learning, probabilistic reasoning 
-			and decision making in uncertain environments. We will discuss the motivations behind common 
-			machine learning algorithms, and the properties that determine whether or not they will work well 
-			for a particular task. You will derive the mathematical underpinnings for many common methods, 
-			as well as apply machine learning to challenges with real data. In doing so, our goal is that you 
-			gain a strong conceptual understanding of machine learning methods that can empower you to pursue 
+			CS 181 provides a broad and rigorous introduction to machine learning, probabilistic reasoning
+			and decision making in uncertain environments. We will discuss the motivations behind common
+			machine learning algorithms, and the properties that determine whether or not they will work well
+			for a particular task. You will derive the mathematical underpinnings for many common methods,
+			as well as apply machine learning to challenges with real data. In doing so, our goal is that you
+			gain a strong conceptual understanding of machine learning methods that can empower you to pursue
 			future theoretical and practical directions.
 
-      		Topics include: supervised learning, ensemble methods and boosting, neural networks,
+			Topics include: supervised learning, ensemble methods and boosting, neural networks,
 			support vector machines, kernel methods, clustering and unsupervised learning, maximum likelihood,
 			graphical models, hidden Markov models, inference methods, reinforcement learning.
 		</p>
 		<p>
-			The material is aimed at an advanced undergraduate level. Students should be comfortable with writing 
-			non-trivial programs (e.g. any course, experience, or willing to self-study beyond CS 50). All staff-provided 
-			scaffolding code will be in Python. Students should also have a background in probability theory (e.g. STAT 110 
+			The material is aimed at an advanced undergraduate level. Students should be comfortable with writing
+			non-trivial programs (e.g. any course, experience, or willing to self-study beyond CS 50). All
+			staff-provided
+			scaffolding code will be in Python. Students should also have a background in probability theory (e.g. STAT
+			110
 			or equivalent), and familiarity with calculus and linear algebra (e.g. AM 22a, Math 21b, or equivalent).
 		</p>
-
-		</p>
-			<strong>Note:</strong> STAT 111 and CS 51 are not required for CS 181, although having these courses would be beneficial for students.
+
 		<p>
+
+			<strong>Note:</strong> STAT 111 and CS 51 are not required for CS 181, although having these courses would
+			be
+			beneficial for students.
+		</p>
 		<p class="link">
 			<a href="{{ site.baseurl }}/syllabus">Full course description with policies.</a>
 		</p>
-		<p> Link to previous years' websites: 
-			<a href=https://harvard-ml-courses.github.io/cs181-web-2022> 2022</a>,
-			<a href=https://harvard-ml-courses.github.io/cs181-web-2021> 2021</a>
+
+		<p>
+			Find links to previous years on the <a href="{{ site.baseurl }}/resources#previous">resources page</a>.
 		</p>
 	</div>
-</section>
+</section>
diff --git a/resources.html b/resources.html
@@ -8,19 +8,41 @@
     <div class="main">
         <h2 class="title">Course Textbook</h2>
         <p>
-            The course notes can be found 
+            The course notes can be found
             <a href="https://github.com/harvard-ml-courses/cs181-textbook/blob/master/Textbook.pdf" target="_blank">
                 here
             </a>.
         </p>
 
+        <h2 class="title" id="previous">Previous years of CS 181</h2>
+        <ul class="lists">
+            <li>
+                <a href="https://harvard-ml-courses.github.io/cs181-web-2023/" target="_blank"
+                    rel="noopener noreferrer">2023</a> by Prof. Weiwei Pan
+            </li>
+            <li>
+                <a href="https://harvard-ml-courses.github.io/cs181-web-2022/" target="_blank"
+                    rel="noopener noreferrer">2022</a> by Prof. Finale Doshi Velez
+            </li>
+            <li>
+                <a href="https://harvard-ml-courses.github.io/cs181-web-2021/" target="_blank"
+                    rel="noopener noreferrer">2021</a> by Prof. Finale Doshi Velez & Prof. David Parkes
+            </li>
+            <li>
+                <a href="https://harvard-ml-courses.github.io/cs181-web-2020/" target="_blank"
+                    rel="noopener noreferrer">2020</a> by Prof. Finale Doshi-Velez
+            </li>
+        </ul>
+
         <h2 class="title">Relevant Textbooks</h2>
         <ul class="lists">
             <li>
-                <a href="http://www.springer.com/us/book/9780387310732" target="_blank">Bishop 2006, Pattern Recognition and Machine Learning</a>
+                <a href="http://www.springer.com/us/book/9780387310732" target="_blank">Bishop 2006, Pattern Recognition
+                    and Machine Learning</a>
             </li>
             <li>
-                <a target="_blank" href="http://harvardcoopbooks.bncollege.com/webapp/wcs/stores/servlet/BNCB_TextbookDetailView?catalogId=10001&item=N&langId=-1&productId=600005699137&storeId=52084">
+                <a target="_blank"
+                    href="http://harvardcoopbooks.bncollege.com/webapp/wcs/stores/servlet/BNCB_TextbookDetailView?catalogId=10001&item=N&langId=-1&productId=600005699137&storeId=52084">
                     Murphy 2012, Machine Learning: A Probabilistic Perspective</a>
             </li>
             <li>
@@ -39,6 +61,6 @@ <h2 class="title">Relevant Textbooks</h2>
                 <a id="sb" target="_blank" href="https://mml-book.github.io/">
                     Deisenroth, Faisal, and Ong 2020, Mathematics for Machine Learning</a>
         </ul>
-        
+
     </div>
-</section>
+</section>
diff --git a/sections.html b/sections.html
@@ -6,55 +6,22 @@
 
 <section class="main-container text">
     <div class="main">
-        <!-- data is configured in _data/all.yaml -->
-
-        <h2>Sections</h2>
         <p>
             CS 181 has weekly sections to reinforce lecture material with extra explanations
             and exercises. Solutions are posted after all sections have taken place.
             Section notes and solutions will be posted on the <a href="{{ site.baseurl }}/schedule">course schedule</a>.
-            A Google Calendar is also embedded <a href="#gcal">below</a>.
         </p>
-        <table class="table">
-            <thead>
-                <tr>
-                    <th>Time</th>
-                    <th>Location</th>
-                    <th>Staff</th>
-                </tr>
-            </thead>
-            <tbody>
-                {% for oh in site.data.all.sections %}
-                <tr>
-                    <td>{{ oh.time }}</td>
-                    <td>{{ oh.location }}</td>
-                    <td>{{ oh.tfs }}</td>
-                </tr>
-                {% endfor %}
-            </tbody>
-        </table>
+        <p>
+            We encourage you to come to staff office hours if you have questions about the course material or the
+            homeworks.
+        </p>
 
-        <h2>Office hours</h2>
-        <table class="table">
-            <thead>
-                <tr>
-                    <th>Time</th>
-                    <th>Location</th>
-                    <th>Staff</th>
-                </tr>
-            </thead>
-            <tbody>
-                {% for oh in site.data.all.ohs %}
-                <tr>
-                    <td>{{ oh.time }}</td>
-                    <td>{{ oh.location }}</td>
-                    <td>{{ oh.tfs }}</td>
-                </tr>
-                {% endfor %}
-            </tbody>
-        </table>
+        <div class="embed-responsive embed-responsive-16by9 w-100">
+            <iframe class="embed-responsive-item"
+                src="https://docs.google.com/spreadsheets/d/e/2PACX-1vQabh8HnxP3V-ThyikOCD2K8xmUeoslgsYMTPL5ECh0T6B-NeFUGxXc-K7UCj-OsEtFhZazyp1Xay5m/pubhtml?gid=1205943684&amp;single=true&amp;widget=true&amp;headers=false"></iframe>
+        </div>
 
-        <div class="embed-responsive embed-responsive-4by3 w-100" id="gcal">
+        <div class="embed-responsive embed-responsive-4by3 w-100">
             <iframe class="embed-responsive-item"
                 src="https://calendar.google.com/calendar/embed?src=c_f69d6d4ef1d31eb8558c88407d946c5970c48f0624a6d840cc22885edf4ab15c%40group.calendar.google.com&ctz=America%2FNew_York"
                 style="border: 0" width="800" height="600" frameborder="0" scrolling="no"></iframe>

diff --git a/staff.html b/staff.html
@@ -10,7 +10,7 @@
         <h2 class="title">People</h2>
         <h3>Instructors</h3>
         <ul class="lists">
-            <li><a href="[email protected]">Finale Doshi-Velez</a></li>
+            <li><a href="mailto:[email protected]">Finale Doshi-Velez</a></li>
             <li><a href="mailto:[email protected]">David Alvarez-Melis</a></li>
         </ul>