Machine Learning Case Studies

Machine Learning Case Studies
In-person recommended

Typical books and classes will teach you how to train a linear regression or a deep neural network model, but they will leave many important questions unanswered. Questions like:

• Is this problem a fit for ML?
• How do I formulate this as a modeling task?
• How do I measure performance?
• What should I do to try to improve my model?
• Am I done, or should I keep trying to improve my model?

We are developing a new tool for training data scientists and ML engineers to answer these questions using ML case studies. This session will be a group discussion. Try to attend in person if you are able. We are still refining the teaching process, and it won't be the same for remote attendees.

This month will be another opportunity to practice understanding parts of the ML lifecycle. Attendees will discuss and brainstorm individually and in groups. This will be an exercise in critical thinking.

We invite learners, mentors, and managers to all participate, and to provide feedback to help us refine the process.

* Please make sure to read the instructions for joining the event below.

Agenda:

• 12:00 - 1:15 pm -- Case study presentation and group discussions (both in-person & zoom, but in-person recommended)
• Time permitting -- Additional Q&A, networking

Links to notes/slides and videos of prior meetups are available on the SDML GitHub repo https://github.com/SanDiegoMachineLearning/talks

Location:
We are continuing to look for an optimal in-person site. For now, we will be meeting inside the ALX Apartments downtown.

Please Note: There are two steps required to join the online meetup:

• You must go to our Slack community and ask for the password for the meeting. Link to join is below.
• You must have a Zoom login in order to join the event. A free Zoom account will work. If you get an error message joining the Zoom, please login to your account on the Zoom website then try again.
• Use this Zoom link: https://us06web.zoom.us/j/82891977558

Community:
Join our slack channel for questions and discussion about what's new in ML:
https://join.slack.com/t/sdmachinelearning/shared_invite/zt-33z4811de-F_YKuCsQH1ev3TLSnQQfgQ