diff --git a/LICENSE.txt b/LICENSE.txt
new file mode 100644
index 0000000..6836a48
--- /dev/null
+++ b/LICENSE.txt
@@ -0,0 +1,21 @@
+The MIT License (MIT)
+
+Copyright (c) 2014 Quinn Liu
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
\ No newline at end of file
diff --git a/README.md b/README.md
index b69a2d9..b12655a 100644
--- a/README.md
+++ b/README.md
@@ -9,6 +9,9 @@ T and some performance measure P, if its performance on T, as measured by P, imp
 
 ~ Definition of Machine Learning by Tom Mitchell
 
+<b>NOTE:</b> If you are interested in building intelligent machines based on biological computation principles please
+check out this project I started called [wAlnut](https://github.com/WalnutiQ/wAlnut).
+
 <h2>How to use this code</h2>
   1. Install [Octave free here](https://db.tt/J97Im052) or [Matlab not free here](http://www.mathworks.com/products/matlab/). Note that Octave = Matlab without the nice graphical user interface. I use Octave so don't feel like you are missing anything if you don't have money for Matlab.
 
@@ -34,4 +37,4 @@ T and some performance measure P, if its performance on T, as measured by P, imp
   - README.md = the file you are reading right now
 
 ===================================================================
-Feel free to e-mail me at quinnliu@vt.edu for any questions. Enjoy!
\ No newline at end of file
+Feel free to e-mail me at quinnliu@vt.edu for any questions. Enjoy!
diff --git a/supervisedLearning/linearRegressionIn1Variable/plotData.m b/supervisedLearning/linearRegressionIn1Variable/plotData.m
index 7f39ed8..3c991c7 100644
--- a/supervisedLearning/linearRegressionIn1Variable/plotData.m
+++ b/supervisedLearning/linearRegressionIn1Variable/plotData.m
@@ -14,4 +14,4 @@ function plotData(x, y)
 ylabel('Profit in $10,000s');            % Set the y-axis label
 xlabel('Population of City in 10,000s'); % Set the x-axis label
 
-end
+end
\ No newline at end of file
diff --git a/supervisedLearning/linearRegressionIn1Variable/run.m b/supervisedLearning/linearRegressionIn1Variable/run.m
index d0b8951..c92cfd7 100644
--- a/supervisedLearning/linearRegressionIn1Variable/run.m
+++ b/supervisedLearning/linearRegressionIn1Variable/run.m
@@ -9,11 +9,11 @@
 %% ======================= Part 1: Plotting =======================
 fprintf('Plotting Data ...\n')
 data = load('inputTrainingSet.txt');
-X = data(:, 1); y = data(:, 2);
+X = data(:, 1); 
+y = data(:, 2);
 m = length(y); % number of training examples
 
 % Plot Data
-% Note: You have to complete the code in plotData.m
 plotData(X, y);
 
 fprintf('Program paused. Press enter to continue.\n');
diff --git a/unsupervisedLearning/neuralNetworks/digitRecognition/README.md b/unsupervisedLearning/neuralNetworks/digitRecognition/README.md
index 72afb43..da9ead3 100644
--- a/unsupervisedLearning/neuralNetworks/digitRecognition/README.md
+++ b/unsupervisedLearning/neuralNetworks/digitRecognition/README.md
@@ -9,10 +9,10 @@ Recognizing Digits with Neural Networks
 3. navigate into the folder with the above files
 
 4. type ```runMultiClassLogisticRegressionNeuralNetwork``` in Octave or Matlab command line to see an example of a trained 
-2 layer neural network to recognize hand written digits with 95% success rate.
+2 layer neural network to recognize MNIST hand written digits with 95% success rate.
 
 5. type ```runMultiClassNeuralNetworkWith3Layers``` in Octave or Matlab command line to see an example of a trained 
-3 layer neural network to recognize hand written digits with 97% sucess rate.
+3 layer neural network to recognize MNIST hand written digits with 97% sucess rate.
 
 <h2>Neural Network Review</h2>
   - Why a new non-linear hypotheses?