Machine Learning (ML) has found it’s way into much of today’s computational landscape and is a powerful tool to extract meaning from the large amounts of data generated by high performance computing. This workshop seeks to arm you with knowledge of existing ML tools you can easily integrate in your research workflow. Our goal is to learn the tools of the trade with hands-on learning.

Everyone is welcome to join at any time, but be prepared to actively participate.

Pizza will be provided by SC2 – just bring your own drink!

Syllabus

The following is a tentative syllabus for the Machine Learning Collaborative Workshop. It is tentative in that the topics listed below are suggestions of what could be covered in the 8 week workshop, but since the instructors will cycle between the participants, each instructor is welcome and encouraged to add or remove topics to their lesson as deemed necessary. The majority of the syllabus is taken from python library scikit-learn’s documentation (http://scikit-learn.org/stable/documentation.html).

I. Introduction

    1. Workshop Overview and Intention
    2. What is Machine Learning (ML)?
    3. Typical Data Formatting
    4. Simple Linear Regression

VIII. Wrap Up

    1. Knowing what algorithm to use when
    2. Other Resources for ML
    3. Brief Overview of ML Topics not Covered
    4. The Latest in ML

LOGISTICS

TIME: Fridays @ 6 – 7 PM

ROOM: EECS 1311 (1301 Beal Ave.)

The workshop will run every Friday from October 6 to December 1, 2017, except November 24.

Pizza provided by the SC2 – just bring your own drink

Questions?