Lead: Sandeep Puthanveetil Satheesan 

Members: Minu Mathew, Vismayak Mohanarajan Todd Nicholson Kastan Day Benjamin Galewsky Volodymyr Kindratenko 

Timeline: April to June 2022

First meeting:  

Regular meeting schedule: Bi-weekly - Mondays from 11:00 am to 11:45 am.

Goals/Topics:

  1. Come up with a set of good hands-on study materials that Research Software Engineers can use to develop and/improve Machine Learning (ML) skills
  2. These materials should include ones that are useful for beginners and people with intermediate skills in ML
  3. Gather documentation on ML models that generally work for different problem areas or are based on some parameters (e.g., amount of training data for supervised learning)

  4. Collate and adapt the collected materials if possible

  5. Document the collected materials / URLs and categorize them (based on the focus groups' criteria)
  6. The focus group may look at specific areas within the ML spectrum to reduce the scope if needed
    1. Traditional ML
      1. Regression
      2. Classification
        1. XGboost and LightGBM
        2. SVM
        3. ..
      3. Data augmentation techniques
    2. DL
      1. Image
      2. Text
      3. Geospatial
      4. Data augmentation techniques
    3. ML Ops
      1. Available tools
      2. Walkthrough tutorial
      3. Data pipelines
    4. Road maps
      1. Explore existing roadmap repositories for ML and review
  7. ML Ops concepts and some suggested services
  8. Write some code examples that can be shared (e.g. Jupyter Notebooks)
  9. Collect documentation on existing NCSA hardware for ML (HAL, Delta)
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