# 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