[WIP][SPARK-3530][MLLIB] pipeline and parameters

### This is a WIP, so please do not spend time on individual lines.

This PR adds package "org.apache.spark.ml" with pipeline and parameters, as discussed on the JIRA. This is a joint work of @jkbradley @etrain @shivaram and many others who helped on the design, also with help from  @marmbrus and @liancheng on the Spark SQL side. The design doc can be found at:

https://docs.google.com/document/d/1rVwXRjWKfIb-7PI6b86ipytwbUH7irSNLF1_6dLmh8o/edit?usp=sharing

Again, though it compiles, this is a WIP. So please do not spend time on individual lines.

**org.apache.spark.ml**

This is a new package with new set of ML APIs that address practical machine learning pipelines. (Sorry for taking so long!) It will be an alpha component, so this is definitely not something set in stone. The new set of APIs, inspired by the MLI project from AMPLab and scikit-learn, takes leverage on Spark SQL's schema support and execution plan optimization. It introduces the following components that help build a practical pipeline:

1. Transformer, which transforms a dataset into another
2. Estimator, which fits models to data, where models are transformers
3. Evaluator, which evaluates model output and returns a scalar metric
4. Pipeline, a simple pipeline that consists of transformers and estimators

Parameters could be supplied at fit/transform or embedded with components.

1. Param: a strong-typed parameter key with self-contained doc
2. ParamMap: a param -> value map
3. Params: trait for components with parameters

For any component that implements `Params`, user can easily check the doc by calling `explainParams`:

~~~
> val lr = new LogisticRegression
> lr.explainParams
maxIter: max number of iterations (default: 100)
regParam: regularization constant (default: 0.1)
labelCol: label column name (default: label)
featuresCol: features column name (default: features)
~~~

or user can check individual param:

~~~
> lr.maxIter
maxIter: max number of iterations (default: 100)
~~~

**Please start with the example code in `LogisticRegressionSuite.scala`, where I put three examples:**

1. run a simple logistic regression job

~~~
    val lr = new LogisticRegression()
      .setMaxIter(10)
      .setRegParam(1.0)
    val model = lr.fit(dataset)
    model.transform(dataset, model.threshold -> 0.8) // overwrite threshold
      .select('label, 'score, 'prediction).collect()
      .foreach(println)
~~~

2. run logistic regression with cross-validation and grid search using areaUnderROC (default) as the metric

~~~
    val lr = new LogisticRegression
    val lrParamMaps = new ParamGridBuilder()
      .addMulti(lr.regParam, Array(0.1, 100.0))
      .addMulti(lr.maxIter, Array(0, 5))
      .build()
    val eval = new BinaryClassificationEvaluator
    val cv = new CrossValidator()
      .setEstimator(lr)
      .setEstimatorParamMaps(lrParamMaps)
      .setEvaluator(eval)
      .setNumFolds(3)
    val bestModel = cv.fit(dataset)
~~~

3. run a pipeline that consists of a standard scaler and a logistic regression component

~~~
    val scaler = new StandardScaler()
      .setInputCol("features")
      .setOutputCol("scaledFeatures")
    val lr = new LogisticRegression()
      .setFeaturesCol("scaledFeatures")
    val pipeline = new Pipeline()
      .setStages(Array(scaler, lr))
    val model = pipeline.fit(dataset)
    val predictions = model.transform(dataset)
      .select('label, 'score, 'prediction)
      .collect()
      .foreach(println)
~~~

Java examples are very similar. See `JavaLogisticRegressionSuite`.

**What are missing now and will be added soon:**

1. Runtime check of schemas. So before we touch the data, we will go through the schema and make sure column names and types match the input parameters.
2. ~~Java examples.~~
3. Serialization.
4. Store training parameters in trained models.
5. Python API.

**What I'm not very confident with and definitely need feedback:**

The usage of parameters is a little messy. The interface I do want to keep is the one that supports multi-model training:

~~~
def fit(dataset: SchemaRDD, paramMaps: Array[ParamMap]): Array[Model]
~~~

which leaves space for the algorithm to optimize the training phrase. This is different from scikit-learn's design, where `fit` returns itself. But for large-scale datasets, we do want to do training in parallel and returns multiple models. Now there are two places you can specify parameters:

1. ~~using the embedded paramMap~~

~~~
lr.set(lr.maxIter, 50)
~~~

2. at fit/transform time:

~~~
lr.fit(dataset, lr.maxIter -> 50)
~~~

The latter overwrites the former. I can change the former to builder pattern

~~~
lr.setMaxIter(50)
~~~

which may look better. But there may be better solutions.
This is useful when we try to store training parameters in trained models.