Add link to FA24 interest form

index.md

@@ -6,16 +6,15 @@ mainpage: true
 
 ## Enrollment Information
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-Enrollment for the course on Student Center begins around the first day of the course (tentatively September 11 for Fall 2024). Please contact the course manager for more information.
-<!-- In the meantime, please fill out [this form](https://forms.gle/eHpqSJhGmQdbccfEA) to be placed on our interest form list. -->
+Enrollment for the course on Student Center begins around the first day of the course (tentatively September 11 for Fall 2024).
+In the meantime, please fill out [this form](https://forms.gle/TE624BDynPsoAkMc7) to be placed on our interest list.
 
-Unfortunately, this semester we are unable to accommodate for students who are at the hard credit limit, or have a scheduling conflict in Student Center.
+Unfortunately, this semester we are unable to accommodate for students who are at their college's credit limit, or have a scheduling conflict in Student Center.
 
 ## Overview
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 INFO 1998 is a ten week, one credit, S/U only course. 
 
-
 The goal of this course is to provide you with a high-level exposure to a wide range of Data Science techniques and Machine Learning models. From the basics of getting your Jupyter environment setup, to manipulating and visualizing data, to building supervised and unsupervised models, this class aims to give you the base intuition and skillset to continue developing and working on ML projects. We hope you exit the course with an understanding of how models and optimization techniques work, as well as have the confidence and tools to solve future problems on your own.
 
 The key to succeed in the course is to try out everything yourself. The lecture and assignments are designed with this in mind — we did our best to convey an experience in which you get a chance to work on many techniques and machine learning models. The course material is designed assuming you have no prior experience in machine learning, but does assume that you have been exposed to solving Computer Science problems at an introductory level (in any flavor / language). Accordingly, we will go over the basics of Python to make sure we are all on the same page. You are expected to put in an appropriate amount of effort to complete the assignments and projects.

syllabus.md

@@ -23,7 +23,7 @@ If none of these office hours fit into your schedule, please post your questions
 
 The goal of this course is to provide you with a high-level exposure to a wide range of Data Science techniques and Machine Learning models, for the purpose of enabling you to solve real problems with machine learning. The course covers getting set up, manipulating and visualizing large datasets, building supervised and unsupervised machine learning models, and a discussion about the various application of these methods in the real world. If you have religiously followed the course throughout the semester, you should expect to have a high-level and intuitive understanding of how data problems could be tackled. You can apply this quick, implementation-oriented toolkit you develop yourself to a variety of fields and problems.
 
-If you are interested in a solid mathematical foundation for data science and machine learning, this class is not sufficient in itself. This course, however, should serve as a head start for you.
+If you are interested in building a solid mathematical foundation for data science and machine learning, this class is not sufficient in and of itself. However, it should serve as a head start for you. We highly encourage you to reach out to course staff if you have any questions about future coursework.
 
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 ### Prerequisites
@@ -45,7 +45,7 @@ page for more information.
 Class material will be posted on our course website, including the assignments, lecture
 slides, notes, and demos.
 
-We will use CMS for assignment / project submissions and feedbacks.
+We will use CMS for assignment / project submissions and feedback.
 
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 ### Course Work