Update README.md

README.md

@@ -3,7 +3,7 @@ Introduction
 This package is a Matlab implementation of the algorithms described in the classical machine learning textbook:
 Pattern Recognition and Machine Learning by C. Bishop ([PRML](http://research.microsoft.com/en-us/um/people/cmbishop/prml/)).
 
-Note: this package requires Matlab **R2016b** or later, since it utilizes a new syntax of Matlab called [Implicit expansion](https://cn.mathworks.com/help/matlab/release-notes.html?rntext=implicit+expansion&startrelease=R2016b&endrelease=R2016b&groupby=release&sortby=descending) (a.k.a. broadcasting in Python).
+Note: this package requires Matlab **R2016b** or latter, since it utilizes a new syntax of Matlab called [Implicit expansion](https://cn.mathworks.com/help/matlab/release-notes.html?rntext=implicit+expansion&startrelease=R2016b&endrelease=R2016b&groupby=release&sortby=descending) (a.k.a. broadcasting in Python).
 
 Description
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@@ -12,7 +12,7 @@ While developing this package, I stick to following principles
 * Succinct: The code is extremely terse. Minimizing the number of lines is a primal target. As a result, the core of the algorithms can be easily spot.
 * Efficient: Many tricks for making Matlab scripts fast were applied (eg. vectorization and matrix factorization). Many functions are even comparable with C implementations. Usually, functions in this package are orders faster than Matlab builtin ones which provide the same functionality (eg. kmeans). If anyone have found any Matlab implementation that is faster than mine, I am happy to further optimize.
 * Robust: Many tricks for numerical stability are applied, such as probability computation in log scale and square root matrix update to enforce matrix symmetry, etc.
-* Learnable: The code is heavily commented. Reference formulas in PRML book are indicated for corresponding code lines. Symbols are in sync with the book.
+* Readable: The code is heavily commented. Reference formulas in PRML book are indicated for corresponding code lines. Symbols are in sync with the book.
 * Practical: The package is designed not only to be easily read, but also to be easily used to facilitate ML research. Many functions in this package are already widely used (see [Matlab file exchange](http://www.mathworks.com/matlabcentral/fileexchange/?term=authorid%3A49739)).
 
 Installation
@@ -21,7 +21,7 @@ Installation
 
 2. Run Matlab and navigate to PRMLT/, then run the init.m script.
 
-3. Run some demos in PRMLT/demo directory. Enjoy!
+3. Try demos in PRMLT/demo directory to verify installation correctness. Enjoy!
 
 FeedBack
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