[SPARK-17095] [Documentation] [Latex and Scala doc do not play nicely]

## What changes were proposed in this pull request?

In Latex, it is common to find "}}}" when closing several expressions at once. [SPARK-16822](https://issues.apache.org/jira/browse/SPARK-16822) added Mathjax to render Latex equations in scaladoc. However, when scala doc sees "}}}" or "{{{" it treats it as a special character for code block. This results in some very strange output.

Author: Jagadeesan <as2@us.ibm.com>

Closes apache#14688 from jagadeesanas2/SPARK-17095.

mllib/src/main/scala/org/apache/spark/ml/feature/PolynomialExpansion.scala

@@ -76,9 +76,11 @@ class PolynomialExpansion @Since("1.4.0") (@Since("1.4.0") override val uid: Str
  * (n + d choose d) (including 1 and first-order values). For example, let f([a, b, c], 3) be the
  * function that expands [a, b, c] to their monomials of degree 3. We have the following recursion:
  *
- * {{{
- * f([a, b, c], 3) = f([a, b], 3) ++ f([a, b], 2) * c ++ f([a, b], 1) * c^2 ++ [c^3]
- * }}}
+ * <p><blockquote>
+ *    $$
+ *    f([a, b, c], 3) &= f([a, b], 3) ++ f([a, b], 2) * c ++ f([a, b], 1) * c^2 ++ [c^3]
+ *    $$
+ * </blockquote></p>
  *
  * To handle sparsity, if c is zero, we can skip all monomials that contain it. We remember the
  * current index and increment it properly for sparse input.

mllib/src/main/scala/org/apache/spark/ml/regression/GeneralizedLinearRegression.scala

@@ -196,9 +196,11 @@ class GeneralizedLinearRegression @Since("2.0.0") (@Since("2.0.0") override val
   /**
    * Sets the regularization parameter for L2 regularization.
    * The regularization term is
-   * {{{
-   *   0.5 * regParam * L2norm(coefficients)^2
-   * }}}
+   * <p><blockquote>
+   *    $$
+   *    0.5 * regParam * L2norm(coefficients)^2
+   *    $$
+   * </blockquote></p>
    * Default is 0.0.
    *
    * @group setParam

mllib/src/main/scala/org/apache/spark/ml/regression/LinearRegression.scala

@@ -338,9 +338,12 @@ class LinearRegression @Since("1.3.0") (@Since("1.3.0") override val uid: String
       /*
          Note that in Linear Regression, the objective history (loss + regularization) returned
          from optimizer is computed in the scaled space given by the following formula.
-         {{{
-         L = 1/2n||\sum_i w_i(x_i - \bar{x_i}) / \hat{x_i} - (y - \bar{y}) / \hat{y}||^2 + regTerms
-         }}}
+         <p><blockquote>
+            $$
+            L &= 1/2n||\sum_i w_i(x_i - \bar{x_i}) / \hat{x_i} - (y - \bar{y}) / \hat{y}||^2
+                 + regTerms \\
+            $$
+         </blockquote></p>
        */
       val arrayBuilder = mutable.ArrayBuilder.make[Double]
       var state: optimizer.State = null

mllib/src/main/scala/org/apache/spark/mllib/clustering/StreamingKMeans.scala

@@ -39,10 +39,14 @@ import org.apache.spark.util.random.XORShiftRandom
  * generalized to incorporate forgetfullness (i.e. decay).
  * The update rule (for each cluster) is:
  *
- * {{{
- * c_t+1 = [(c_t * n_t * a) + (x_t * m_t)] / [n_t + m_t]
- * n_t+t = n_t * a + m_t
- * }}}
+ * <p><blockquote>
+ *    $$
+ *    \begin{align}
+ *     c_t+1 &= [(c_t * n_t * a) + (x_t * m_t)] / [n_t + m_t] \\
+ *     n_t+t &= n_t * a + m_t
+ *    \end{align}
+ *    $$
+ * </blockquote></p>
  *
  * Where c_t is the previously estimated centroid for that cluster,
  * n_t is the number of points assigned to it thus far, x_t is the centroid