Contents

Welcome to Introduction to Machine Learning Applications

IMPORTANT LINKS

SECTION 1

  • Prof Kuruzovich WEBEX Meetings Classroom Link https://rensselaer.webex.com/meet/kuruzj

  • Box File link

  • Office Hours Tuesday 2:00 PM – 4:00 PM, or by appointment . Please Message me in the Kuruzovich Office Hours Space on Webex Teams.

  • TA Shailesh Divey, Office Hours Tuesday 3:00 PM – 5:00 PM, or by appointment, email: diveys@rpi.edu

SECTION 2

This is the website for the Rensselaer class Introduction to Machine Learning Applications. Both sections 01 and 02.

In order to be able to reach the Box file share, you will need an RPI Box Account. Please sign up for it here.

During these challenging times we are going to do our best to provide an engaging learning experience. We will be useing a combination of tools for the class and I wanted to provide that overview here.

For class this semester, thank you in advance for complying with RPI mask policies. The science is solid and we want to all get through the semester heathy and without incident.

Here is a quick reference for the systems that will be used in the class. Please download the desktop copy of both Webex Meetings and Webex Teams via this link.

Webex Meetings: Hybrid Class Streaming

We will be using Webex Meetings to stream all classes. The course recordings be made available through this website. WEBEX Meetings Classroom Link

Webex Teams: Classroom and Asynchronous Discussions and Questions

we want to be very responsive to your questions and concerns as we go through the class. Webex Teams (NOT EMAIL) is the best way to reach me. We would suggest downloading the Webex Teams application for your phone and desktop. Because Webex meeting chats do not persist, we are going to use the Webex Teams space for text discussions even when streaming from Webex meetings. WEBEX TEAMS Discussion Space

This semester could be challenging if you don’t know others from the class and are working remotely. Feel free to use this as a drop in space. Create a meeting and collaborate with others.

Course Content

This website will be the primary source of content for the class. This includes readings, videos, Excel files, Jupyter notebooks, etc.

  • Course Schedule This covers when assignments are do.

  • Sessions Each day will summaries the variety of different activities on the session page.

  • Assignments. All instructions for assignments will be provided in this website.

  • Google drive will be used to share files (presentations, spreadsheets, etc.).

LMS: Assignment Submission, Quizzes, and Grading

  • Please be aware of the variety of course assignments listed on the schedule. We might even through a few quizes in there. These will done through the LMS.

  • In some cases, a starter excel spreadsheet or Jupyter notebook will be provided. Always upload the actual completed .xlsx file or .ipynb file for grading rather than a pdf. RPI LMS

Previous versions of the class can be found at:

Spring 2020 https://rpispring2020.analyticsdojo.com/

Fall 2019 https://rpifall2019.analyticsdojo.com/

Schedule