Plan of Study (Fall 2019) @ Virginia Tech DLRL

September

Paper Reading:

  • Q-Learning
  • Recurrent Neural Networks

Get Familiar With:

  • Markov Models (as well as Hidden Variants)
  • Neural Networks
  • Cloud Machine Learning Pipelines (likely AWS)

October

Paper Reading:

  • Additional papers on Recurrent Neural Networks as advised by Dr. Xuan.

Goals & Implementation:

  • Jupyter/Colaboratory Notebooks
    • Basics of Tensorflow
    • Introduction to Markov Models

Goals & Implementation

  • Whitepaper: Machine Learning Pipelines on AWS
    • Nominal demonstration of a Deep Learning Pipeline from Data Collection to usable Intelligence.

November

Paper Reading

  • Additional papers on Recurrent Neural Networks as advised by Dr. Xuan, with more of a frame on applications

Goals & Implementation

  • Whitepaper: Machine Learning Data Engineering for Biology
    • A Systems-level approach on identifying and tackling a Biology problem with Machine Learning
    • Discussion points on HIPAA compliance for Bio-companies and universities when operating in this space.

December

Goals & Implementation

  • Nominal Implementation of the Biology Machine Learning Framework
    • Approaches and considerations to gathering Data
    • Identifying the problem and how to determine what ML methodologies to tackle them
    • How to make your models scalable, available, and secure to stakeholders
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