[ View menu ]
Main

Summer Workshop in Machine Learning at Carnegie Mellon, May 26-26, 2019

Filed in Programs
Subscribe to Decision Science News by Email (one email per week, easy unsubscribe)

APPLICATION DEADLINE: APRIL 12, 2019

Machine learning is impacting the business world and the business research community. The CMU Summer Workshop on Machine Learning is intended to introduce junior researchers to the cutting-edge machine learning methods and their applications in the marketing and information systems research, and open up common ground for future discussions among researchers who are using or wish to use machine learning methods in their research.

The format of the workshop is a combination of lecture-style sessions, hands-on tutorials (directed primarily at doctoral students) and panel presentations. The lecture-style sessions and hands-on tutorials will cover the following topics:

  • Supervised Learning
  • Unsupervised Learning
  • Graphical Models
  • Deep Learning
  • Reinforcement Learning
  • Text, Image, Audio, Video mining
  • Bayesian Machine Learning
  • Causal Inference

Who should participate: The workshop is primarily intended for Ph.D. students in Consumer Behavior, Information Systems, Quantitative Marketing, or related business disciplines. We also welcome junior faculty members with an interest in machine learning methods to participate in the workshop.

Student application deadline is April 12, 2019.  We will admit a total of 50 students; accepted applicants will be notified by April 20, 2019.  Submit student application.

If there are more than two students who apply from the same school, we may ask the school to rank the students. Due to limited slots, we guarantee top 2 will be accepted, but depending on demand others may or may not be accepted to the workshop. Priority may be given to ISMS members.

Registration deadline is April 26, 2019:  Ph.D. Students $50, Faculty $200.

All attendees must cover their own travel expenses and hotel costs. Meals during the workshop will be provided.

Conference Chairs: Yan Huang (CMU, yanhuang@andrew.cmu.edu) and Xiao Liu (NYU, xliu@stern.nyu.edu)

0 Comments

No comments

RSS feed Comments

Write Comment

XHTML: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>