Update README.md

Author: Cheng-Lin-Li

README.md

@@ -11,15 +11,15 @@ Thanks,
 
 |Algorithm|Description|Link|
 |------|------|--------|
-|Decision Tree|By measuring information gain via calculating the entropy of previous observations, decision tree algorithm may help us to predict the decision or results|[Specification](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/DecisionTree/TechnicalSpecification-%5BDecisionTree%5D-%5B1.1%5D-%5B20160929%5D.pdf) and [Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/DecisionTree)|
-|Fast Map|An approach for dimensions reductions|[Specification](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/FastMap/TechnicalSpecification-%5BPCA_FastMap%5D-%5B1.0%5D-%5B20160929%5D.pdf) and [Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/FastMap)|
-|Gaussian Mixture Models (GMMs)|GMMs are among the most statistically mature methods for data clustering (and density estimation)|[Specification](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/GMM/INF552-TechnicalSpecification-%5Bk-means_EM-GMM%5D-%5B1.2%5D-%5B20170515%5D.pdf) and [Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/GMM)|
-|Hierarchical clustering (HAC)|HCA seeks to build a hierarchy of clusters from bottom up or top down. This is a bottom up implementation.|[Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/HAC)|
-|Hidden Markov model (HMM) and Viterbi|HMM is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (i.e. hidden) states. The Viterbi algorithmis used to compute the most probable path (as well as its probability). It requires knowledge of the parameters of the HMM model and a particular output sequence and it finds the state sequence that is most likely to have generated that output sequence. It works by finding a maximum over all possible state sequences. In sequence analysis, this method can be used for example to predict coding vs non-coding sequences.|[Specification](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/HMM/INF552-TechnicalSpecification-%5BHMM%5D-%5B1.0%5D-%5B20161203%5D.pdf) and [Viterbi Algorithm Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/HMM)|
-|K-Means|One of most famous and easy to understand clustering algorithm|[Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/K-Means)|
+|[Decision Tree](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/DecisionTree)|By measuring information gain via calculating the entropy of previous observations, decision tree algorithm may help us to predict the decision or results|[Specification](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/DecisionTree/TechnicalSpecification-%5BDecisionTree%5D-%5B1.1%5D-%5B20160929%5D.pdf) and [Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/DecisionTree/DecisionTree.py)|
+|[Fast Map](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/FastMap)|An approach for dimensions reductions|[Specification](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/FastMap/TechnicalSpecification-%5BPCA_FastMap%5D-%5B1.0%5D-%5B20160929%5D.pdf) and [Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/FastMap/FastMap.py)|
+|[Gaussian Mixture Models (GMMs)](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/GMM)|GMMs are among the most statistically mature methods for data clustering (and density estimation)|[Specification](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/GMM/INF552-TechnicalSpecification-%5Bk-means_EM-GMM%5D-%5B1.2%5D-%5B20170515%5D.pdf) and [Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/GMM/GMM.py)|
+|[Hierarchical clustering (HAC)](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/HAC)|HCA seeks to build a hierarchy of clusters from bottom up or top down. This is a bottom up implementation.|[Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/HAC/HAC.py)|
+|[Hidden Markov model (HMM) and Viterbi](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/HMM)|HMM is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (i.e. hidden) states. The Viterbi algorithmis used to compute the most probable path (as well as its probability). It requires knowledge of the parameters of the HMM model and a particular output sequence and it finds the state sequence that is most likely to have generated that output sequence. It works by finding a maximum over all possible state sequences. In sequence analysis, this method can be used for example to predict coding vs non-coding sequences.|[Specification](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/HMM/INF552-TechnicalSpecification-%5BHMM%5D-%5B1.0%5D-%5B20161203%5D.pdf) and [Viterbi Algorithm Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/HMM/HMM-Viterbi.py)|
+|[K-Means](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/K-Means)|One of most famous and easy to understand clustering algorithm|[Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/K-Means/K-Means.py)|
 |Neural Network|The foundation algorithm of deep learning|[Specification](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/NeuralNetwork/INF552-TechnicalSpecification-%5BNeuralNetwork%5D-%5B1.0%5D-%5B20161104%5D.pdf) and [Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/NeuralNetwork)|
-|PCA|An algorithm for dimension reductions|[Specification](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/PCA/INF552-TechnicalSpecification-PCA_FastMap-%5B1.0%5D-%5B20161011%5D.pdf) and [Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/PCA)|
-|Neural Network and Long Short Term Memory (LSTM)|This is a project which implemented Neural Network and Long Short Term Memory (LSTM) to predict stock price in Python 3 by Tensorflow|[Project Report](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/TensorFlow/ProjectReport.pdf) and [Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/TensorFlow)|
+|[PCA](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/PCA)|An algorithm for dimension reductions|[Specification](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/PCA/INF552-TechnicalSpecification-PCA_FastMap-%5B1.0%5D-%5B20161011%5D.pdf) and [Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/PCA/PCA.py)|
+|[Neural Network and Long Short Term Memory (LSTM) on Tensorflow](https://github.com/Cheng-Lin-Li/MachineLearning/tree/master/TensorFlow)|This is a project which implemented Neural Network and Long Short Term Memory (LSTM) to predict stock price in Python 3 by Tensorflow|[Project Report](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/TensorFlow/ProjectReport.pdf) and [MLP Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/TensorFlow/StockPriceForecasting-MLP.py), [LSTM Source Code](https://github.com/Cheng-Lin-Li/MachineLearning/blob/master/TensorFlow/StockPriceForecasting-LSTM.py)|