added comments and MIT license

Author: Quinn Liu

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.

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

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');

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?