<h1 id="september">September</h1>
<h2 id="paper-reading-">Paper Reading:</h2>
<ul>
<li>Q-Learning</li>
<li>Recurrent Neural Networks</li>
</ul>
<h2 id="get-familiar-with-">Get Familiar With:</h2>
<ul>
<li>Markov Models (as well as Hidden Variants)</li>
<li>Neural Networks</li>
<li>Cloud Machine Learning Pipelines (likely AWS)</li>
</ul>
<h1 id="october">October</h1>
<h2 id="paper-reading-">Paper Reading:</h2>
<ul>
<li>Additional papers on Recurrent Neural Networks as advised by Dr. Xuan.</li>
</ul>
<h2 id="goals-implementation-">Goals &amp; Implementation:</h2>
<ul>
<li>Jupyter/Colaboratory Notebooks<ul>
<li>Basics of Tensorflow</li>
<li>Introduction to Markov Models</li>
</ul>
</li>
</ul>
<h2 id="goals-implementation">Goals &amp; Implementation</h2>
<ul>
<li>Whitepaper: Machine Learning Pipelines on AWS<ul>
<li>Nominal demonstration of a Deep Learning Pipeline from Data Collection to usable Intelligence.</li>
</ul>
</li>
</ul>
<h1 id="november">November</h1>
<h2 id="paper-reading">Paper Reading</h2>
<ul>
<li>Additional papers on Recurrent Neural Networks as advised by Dr. Xuan, with more of a frame on applications</li>
</ul>
<h2 id="goals-implementation">Goals &amp; Implementation</h2>
<ul>
<li>Whitepaper: Machine Learning Data Engineering for Biology<ul>
<li>A Systems-level approach on identifying and tackling a Biology problem with Machine Learning</li>
<li>Discussion points on HIPAA compliance for Bio-companies and universities when operating in this space.</li>
</ul>
</li>
</ul>
<h1 id="december">December</h1>
<h2 id="goals-implementation">Goals &amp; Implementation</h2>
<ul>
<li>Nominal Implementation of the Biology Machine Learning Framework<ul>
<li>Approaches and considerations to gathering Data</li>
<li>Identifying the problem and how to determine what ML methodologies to tackle them</li>
<li>How to make your models scalable, available, and secure to stakeholders</li>
</ul>
</li>
</ul>


# 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

Plan of Study (Fall 2019) @ Virginia Tech DLRL

Guru of Grok;
Lover of Lifts;
Peloton Pedaler;
Constantly Consuming Coffee; 
Crafter of Code, Containers, and Clouds!
Absolutely adore alliteration
