There is a lot of buzz and mystery around machine learning. D-Lab's Machine Learning Working Group is a great space to come learn about it or share your knowledge and projects with others.  

Spring 2018, the Machine Learning Working Group (MLWG) meets every other Wednesday in the D-Lab Convening Room (356B Barrows Hall) from 3-5PM.  This semester, we will be getting back to basics teaching the beginner walkthroughs of algorithms you can immediately apply to your own research in both Python and R. On January 31, the k-nearest neighbors method was the topic of our first meeting this semester. Thanks to all of you who attended.  We still have several other methods to review this semester, including decision tree (February 14), random forest (February 28), gradient boosting (April 11), and elastic net (April 25) algorithms. Feel free to bring your own data or project questions as there is often time for co-working during the second half of the working group meetings.

Graduate and undergraduate students, staff, and faculty alike are all welcome to attend. We also encourage you to participate in 10-12 minute lightning talks about anything machine learning-related on March 14 and/or May 9. We are always looking for presenters to lead these coding walkthroughs, to organize and participate in Kaggle competitions, and to help design our upcoming e-book. Please contact Evan Muzzall (evan.muzzall@berkeley.edu) if you are interested!

Of great significance to those interested in machine learning, be sure to check out D-Lab's Computational Text Analysis Working Group and Cloud Computing Working Group. If you are just getting started with these methods, you may want to first check out the D-Lab's Python, R, and Text Analysis FUN!damentals introductory series. Visit the D-Lab calendar and  to see upcoming offerings and to register.  For one-to-one advising, visit our consulting services page.

Author: 

Evan Muzzall

Evan earned his PhD in Biological Anthropology from Southern Illinois University Carbondale where he focused on spatial patterns of skeletal and dental variation in two large necropoles of Iron Age Central Italy (1st millennium BC). His current research focuses on how environmental and cultural influences affect "normal" skeletal and dental developmental trajectories and machine learning. He is the Instructional Services Lead at the D-Lab and teaches several of the R workshop trainings.