Update README.md

README.md

@@ -3,29 +3,29 @@ 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 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).
 
 Description
 -------
 The design goal of the code are as follows:
 
-1. Succinct: Code is extremely terse. Minimizing the number of line of code is one of the primal target. As a result, the core of the algorithms can be easily spot.
-2. Efficient: Many tricks for making Matlab scripts fast were applied (eg. vectorization and matrix factorization). Many functions are even comparable with C implementation. Usually, functions in this package are orders faster than Matlab builtin functions which provide the same functionality (eg. kmeans). If anyone found any Matlab implementation that is faster than mine, I am happy to further optimize.
-3. Robust: Many numerical stability techniques are applied, such as probability computation in log scale to avoid numerical underflow and overflow, square root form update of symmetric matrix, etc.
-4. Easy to learn: The code is heavily commented. Reference formulas in PRML book are indicated for corresponding code lines. Symbols are in sync with the book.
-5. 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)).
+* 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.
+* 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
 -------
-1. Download the package by running: `git clone https://github.com/PRML/PRMLT.git`.
+1. Download the package to your local path (e.g. PRMLT/) by running: `git clone https://github.com/PRML/PRMLT.git`.
 
-2. Run Matlab and navigate to package location as working directory, then run the init.m script.
+2. Run Matlab and navigate to PRMLT/, then run the init.m script.
 
-3. Run some demos in the your_location/demo directory. Enjoy!
+3. Run some demos in PRMLT/demo directory. Enjoy!
 
 FeedBack
 -------
-If you found any bug or have any suggestion, please do fire issues. I am graceful for any feedback and will do my best to improve this package.
+If you found any bug or have any suggestion, please do file issues. I am graceful for any feedback and will do my best to improve this package.
 
 License
 -------