Plan for Fall 2019:
September:
October:
November:
December:
From Sep 6th - Oct 6th
From Oct 7th - Nov 7th
From Nov 8th to Dec 8th
From Dec 9th - Dec 20th
Paper Reading:
1.Q-Learning
2.Recurrent Neural Network
Prerequisites:
1.Markov Model
2.Hidden Markov Model
3.Neural Network
Get Familar with:
1. Python Gym environents
2.Tensorflow
Code Implement:
1. python gym Lunar Lander simulator(Q-learning)
2. AWS
Paper Reading:
1. Q-learning with MSA
2. RNN with LSTM
3. Deep RNN on protein function prediction
Code Implement:
1. Try different Learning Mechanism on the Lunar Lander simulator
2. AWS
Paper Reading:
1. Parallel training for RNN
2. Ask advisor for papers if I have no problems understanding those before
Paper Reading:
1. Ask Dr. Neuwald for papers about related field
Attention: Talking to Dr. Xuan and Dr. Neuwald for both machine learning and biomedical consulting anytime when you think you get stuck
Reading List:
September: Hidden Markov Models
Q-Learning Technical Note
Reinforcement Learning: A tutorial
October: A Critical Review of Recurrent Neural Networks
A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures
Deep Recurrent Neural Network for Protein Function Prediction From Sequence
November: A Q-Leaning Approach for Aligning Protein Sequences
Alignment of Protein Sequences by their profiles
Learning Rates For Q-Learning