update MLlib page for 1.5

2 files changed, 28 insertions, 22 deletions

diff --git a/mllib/index.md b/mllib/index.md
index da0288db1..7eebb8173 100644
--- a/mllib/index.md
+++ b/mllib/index.md
@@ -14,7 +14,7 @@ subproject: MLlib
   <div class="col-md-7 col-sm-7">
     <h2>Ease of Use</h2>
     <p class="lead">
-      Usable in Java, Scala and Python.
+      Usable in Java, Scala, Python, and SparkR.
     </p>
     <p>
       MLlib fits into <a href="{{site.url}}">Spark</a>'s
@@ -83,22 +83,25 @@ subproject: MLlib
   <div class="col-md-4 col-padded">
     <h3>Algorithms</h3>
     <p>
-      MLlib 1.3 contains the following algorithms:
+      MLlib contains the following algorithms and utilities:
     </p>
     <ul class="list-narrow">
-      <li>linear SVM and logistic regression</li>
+      <li>logistic regression and linear support vector machine (SVM)</li>
       <li>classification and regression tree</li>
       <li>random forest and gradient-boosted trees</li>
-      <li>recommendation via alternating least squares</li>
-      <li>clustering via k-means, Gaussian mixtures, and power iteration clustering</li>
-      <li>topic modeling via latent Dirichlet allocation</li>
-      <li>singular value decomposition</li>
-      <li>linear regression with L<sub>1</sub>- and L<sub>2</sub>-regularization</li>
+      <li>recommendation via alternating least squares (ALS)</li>
+      <li>clustering via k-means, Gaussian mixtures (GMM), and power iteration clustering</li>
+      <li>topic modeling via latent Dirichlet allocation (LDA)</li>
+      <li>singular value decomposition (SVD) and QR decomposition</li>
+      <li>principal component analysis (PCA)</li>
+      <li>linear regression with L<sub>1</sub>, L<sub>2</sub>, and elastic-net regularization</li>
       <li>isotonic regression</li>
-      <li>multinomial naive Bayes</li>
-      <li>frequent itemset mining via FP-growth</li>
-      <li>basic statistics</li>
+      <li>multinomial/binomial naive Bayes</li>
+      <li>frequent itemset mining via FP-growth and association rules</li>
+      <li>sequential pattern mining via PrefixSpan</li>
+      <li>summary statistics and hypothesis testing</li>
       <li>feature transformations</li>
+      <li>model evaluation and hyper-parameter tuning</li>
     </ul>
     <p>Refer to the <a href="{{site.url}}docs/latest/mllib-guide.html">MLlib guide</a> for usage examples.</p>
   </div>
diff --git a/site/mllib/index.html b/site/mllib/index.html
index 02b6065c0..de3916aad 100644
--- a/site/mllib/index.html
+++ b/site/mllib/index.html
@@ -178,7 +178,7 @@
   <div class="col-md-7 col-sm-7">
     <h2>Ease of Use</h2>
     <p class="lead">
-      Usable in Java, Scala and Python.
+      Usable in Java, Scala, Python, and SparkR.
     </p>
     <p>
       MLlib fits into <a href="/">Spark</a>'s
@@ -250,22 +250,25 @@
   <div class="col-md-4 col-padded">
     <h3>Algorithms</h3>
     <p>
-      MLlib 1.3 contains the following algorithms:
+      MLlib contains the following algorithms and utilities:
     </p>
     <ul class="list-narrow">
-      <li>linear SVM and logistic regression</li>
+      <li>logistic regression and linear support vector machine (SVM)</li>
       <li>classification and regression tree</li>
       <li>random forest and gradient-boosted trees</li>
-      <li>recommendation via alternating least squares</li>
-      <li>clustering via k-means, Gaussian mixtures, and power iteration clustering</li>
-      <li>topic modeling via latent Dirichlet allocation</li>
-      <li>singular value decomposition</li>
-      <li>linear regression with L<sub>1</sub>- and L<sub>2</sub>-regularization</li>
+      <li>recommendation via alternating least squares (ALS)</li>
+      <li>clustering via k-means, Gaussian mixtures (GMM), and power iteration clustering</li>
+      <li>topic modeling via latent Dirichlet allocation (LDA)</li>
+      <li>singular value decomposition (SVD) and QR decomposition</li>
+      <li>principal component analysis (PCA)</li>
+      <li>linear regression with L<sub>1</sub>, L<sub>2</sub>, and elastic-net regularization</li>
       <li>isotonic regression</li>
-      <li>multinomial naive Bayes</li>
-      <li>frequent itemset mining via FP-growth</li>
-      <li>basic statistics</li>
+      <li>multinomial/binomial naive Bayes</li>
+      <li>frequent itemset mining via FP-growth and association rules</li>
+      <li>sequential pattern mining via PrefixSpan</li>
+      <li>summary statistics and hypothesis testing</li>
       <li>feature transformations</li>
+      <li>model evaluation and hyper-parameter tuning</li>
     </ul>
     <p>Refer to the <a href="/docs/latest/mllib-guide.html">MLlib guide</a> for usage examples.</p>
   </div>