minor changes to schedule

schedule.md

@@ -112,7 +112,7 @@ Practice with Plain Language
 {% enddetails %}
 
 {: .week}
-# Week 3 (10/9-10/13): Accessibility Assessment 1: Guidelines and Assessment
+# Week 3 (10/9-10/13): Guidelines and Assessment
 
 {% details Learning Goals %} 
 - What are the current accessibility standards
@@ -143,6 +143,7 @@ Practice with Plain Language
    - Read about [Switches](https://axesslab.com/switches/)
    - Try out the [Image ALT Text Tutorial](https://webaim.org/techniques/alttext/) 
    - Watch: [Rescribe: Authoring and Automatically Editing Audio Descriptions](https://www.youtube.com/watch?v=jmyPmT5T0PY)
+   - Read about [Making numbers accessible](https://www.tpgi.com/making-numbers-in-web-content-accessible/)n
 
 {: .homework} [Class Participation survey](TBD)
 : 
@@ -178,7 +179,7 @@ Practice with Accessibility Assesssment
 {% enddetails %}
 
 {: .week}
-# Week 4 (10/16-10/20): Building Accessible Interfaces & Remediating Problems
+# Week 4 (10/16-10/20): Building & Remediating Accessible Interfaces
 
 {% details Learning Goals %} 
 - How accessibility works under the hood
@@ -301,6 +302,13 @@ TBD
 # Week 6 (10/30 - 11/3): Accessible Visulaizations & AR/VR
 {% details Learning Goals %} 
 
+Bias in Machine Learning
+    - What is Machine Learning (ML)? 
+    - What are the components of ML?
+    - How do we collect data? Who do we collect the data from?
+    - Is the data "good"?
+    - How do we minimize disability bias?
+
 Accessible Data Visualizations
     - What are the commonly-used techniques to make data visualizations accessible?
     - What are the pros and cons of these techniques?
@@ -363,17 +371,10 @@ Check in to make sure you have a plan and it is do-able
 
 
 {: .week}
-# Week 7 (11/6 - 11/10): Fabrication & Data Equity
+# Week 7 (11/6 - 11/10): Fabrication
 {% details Learning Goals %} 
 - Work with laser cutting to make accessible technology
 - Learn about fabrication tools and techniques
-
-- Bias in Machine Learning
-    - What is Machine Learning (ML)? 
-    - What are the components of ML?
-    - How do we collect data? Who do we collect the data from?
-    - Is the data "good"?
-    - How do we minimize disability bias?
 {% enddetails %}