HPC Monthly Workshop: Machine Learning and BIG DATA

July 31-August 1, 2023

The Pittsburgh Supercomputing Center is pleased to present a Machine Learning and Big Data workshop, sponsored by ACCESS.

This workshop will focus on topics including big data analytics and machine learning with Spark, and deep learning using Tensorflow.

This will be an IN PERSON event hosted by various satellite sites, there WILL NOT be a direct to desktop option for this event.  The satellite site list(subject to change) is as follows:

  • Boston University
  • California State University, San Bernardino
  • Carnegie Mellon University (SOLD OUT)
  • University of Cincinnati
  • University of Colorado – Boulder
  • Georgia State University
  • Georgia Institute of Technology
  • Institute for Cyber-Enabled Research at Michigan State University
  • National Center for Supercomputing Applications (SOLD OUT)
  • New York University
  • University of South Carolina Research Computing
  • Texas Tech University
  • Tufts University
  • Yale Center for Research Computing

Registration

Interested applicants must first have an ACCESS ID.  If you do not have an ACCESS ID, please visit this page to create one:

ACCESS USER REGISTRATION

Once you have an ACCESS ID, please complete the following registration page by Friday, July 28 at Noon Eastern time:

DO NOT COMPLETE THE EVENTBRITE REGISTRATION UNTIL YOU HAVE AN ACCESS ID – WE CANNOT REGISTER YOU FOR THIS EVENT WITHOUT A VALID ACCESS ID

Eventbrite Reservation

Classroom location and further details will be provided once your registration has been processed.  Thank you for your patience.

FAQ

 

Tentative Agenda

Monday, July 31
All times given are Eastern
11:00 Welcome
11:25 A Brief History of  Big Data
12:20 Intro to Spark
1:00 Lunch break
2:00 More Spark and Exercises
3:00 Intro to Machine Learning
5:00 Adjourn
Tuesday, August 1
All times given are Eastern
11:00 Machine Learning: Recommender System with Spark
1:00 Lunch break
2:00 Deep Learning with Tensorflow
5:00 Tying it All Together
5:30 Adjourn