[DOCS] Add Algorithms reference from previous docs

closes #1068

Author: Baunsgaard

docs/README.md

@@ -28,12 +28,11 @@ The Documentation is separated into different parts by sub folders.
 .
 ├── _layouts
 │   ├── global.html # The Default content layout and html file.
+│   ├── site.html   # The Default content layout for docs pages contained in site folder
 ├── _site # If run locally a _site folder is generated containing the compiled site.
 ├── _config.yml # The configuration for jekyll to construct the site.
 ├── Gemfile.yml # The dependency list for the documentation
 ├── index.md #  The main entry File
-├── _layouts
-│   ├── global.html
 ├── css # The style files folder
 ├── img # The Images folder
 ├── js  # The JavaScript folder

docs/img/algorithms-reference/computation-of-quartiles-median-and-interquartile-mean.png

@@ -31,16 +31,16 @@ SystemDS's distinguishing characteristics are:
 This version of SystemDS supports: Java 8+,  Python 3.5+, Hadoop 2.6+ (Not 3.X), and Spark 2.1+ (Not 3.X) Nvidia CUDA 10.2
  (CuDNN 7.x) Intel MKL (<=2019.x).
 
-# Links
+## Links
 
 Various forms of documentation for SystemDS are available.
 
-- a [DML language reference](./site/dml-language-reference) for an list of operations possible inside SystemDS.
-- [builtin functions](./site/builtins-reference) contains a collection of builtin functions providing an high level abstraction on complex machine learning algorithms.
+- a [DML Language Reference](./site/dml-language-reference) for an list of operations possible inside SystemDS.
+- [Builtin Functions](./site/builtins-reference) contains a collection of builtin functions providing an high level abstraction on complex machine learning algorithms.
+- [Algorithm Reference](./site/algorithms-reference) contains specifics on algorithms supported in systemds.
 - [Entity Resolution](./site/entity-resolution) provides a collection of customizable entity resolution primitives and pipelines.
 - [Run SystemDS](./site/run) contains an Helloworld example along with an environment setup guide.
 - Instructions on python can be found at [Python Documentation](./api/python/index)
-- The [javadoc API](./api/java/index) contains internal documentation of the system source code.
+- The [JavaDOC](./api/java/index) contains internal documentation of the system source code.
 - [Install from Source](./site/install) guides through setup from git download to running system.
 - If you want to contribute take a look at [Contributing](https://github.com/apache/systemds/blob/master/CONTRIBUTING.md)
-

docs/img/algorithms-reference/example-tree.png

@@ -0,0 +1,122 @@
+---
+layout: site
+title: Algorithms Reference Bibliography
+---
+<!--
+{% comment %}
+Licensed to the Apache Software Foundation (ASF) under one or more
+contributor license agreements.  See the NOTICE file distributed with
+this work for additional information regarding copyright ownership.
+The ASF licenses this file to you under the Apache License, Version 2.0
+(the "License"); you may not use this file except in compliance with
+the License.  You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+{% endcomment %}
+-->
+
+**[AcockStavig1979]** Alan C. Acock and Gordon
+R. Stavig, A Measure of Association for Nonparametric
+Statistics, Social Forces, Oxford University
+Press, Volume 57, Number 4, June, 1979,
+1381--1386.
+
+**[AgrawalKSX2002]** Rakesh Agrawal and
+Jerry Kiernan and Ramakrishnan Srikant and Yirong Xu,
+Hippocratic Databases, Proceedings of the 28-th
+International Conference on Very Large Data Bases (VLDB 2002),
+Hong Kong, China, August 20--23, 2002,
+143--154.
+
+**[Agresti2002]** Alan Agresti, Categorical
+Data Analysis, Second Edition, Wiley Series in
+Probability and Statistics, Wiley-Interscience
+2002, 710.
+
+**[AloiseDHP2009]** Daniel Aloise and Amit
+Deshpande and Pierre Hansen and Preyas Popat, NP-hardness of
+Euclidean Sum-of-squares Clustering, Machine Learning,
+Kluwer Academic Publishers, Volume 75, Number 2,
+May, 2009, 245--248.
+
+**[ArthurVassilvitskii2007]**
+k-means++: The Advantages of Careful Seeding, David
+Arthur and Sergei Vassilvitskii, Proceedings of the 18th
+Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2007),
+January 7--9, 2007, New Orleans, LA,
+USA, 1027--1035.
+
+**[Breiman2001]** L. Breiman. Random forests. Machine Learning, 45(1):5–32, 2001.
+
+**[BreimanFOS1984]** L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. Classification and Regression Trees. Wadsworth, 1984.
+
+**[Chapelle2007]** Olivier Chapelle, Training a Support Vector Machine in the Primal, Neural Computation, 2007.
+
+**[Cochran1954]** William G. Cochran,
+Some Methods for Strengthening the Common $\chi^2$ Tests,
+Biometrics, Volume 10, Number 4, December
+1954, 417--451.
+
+**[Collett2003]** D. Collett. Modelling Survival Data in Medical Research, Second Edition. Chapman & Hall/CRC Texts in Statistical Science. Taylor & Francis, 2003.
+
+**[Gill2000]** Jeff Gill, Generalized Linear
+Models: A Unified Approach, Sage University Papers Series on
+Quantitative Applications in the Social Sciences, Number 07-134,
+2000, Sage Publications, 101.
+
+**[Hartigan1975]** John A. Hartigan,
+Clustering Algorithms, John Wiley~&~Sons Inc.,
+Probability and Mathematical Statistics, April
+1975, 365.
+
+**[Hsieh2008]** C-J Hsieh, K-W Chang, C-J Lin, S. S. Keerthi and S. Sundararajan, A Dual Coordinate Descent Method for Large-scale Linear SVM, International Conference of Machine Learning (ICML), 2008.
+
+**[Lin2008]** Chih-Jen Lin and Ruby C. Weng and
+S. Sathiya Keerthi, Trust Region Newton Method for
+Large-Scale Logistic Regression, Journal of Machine Learning
+Research, April, 2008, Volume 9, 627--650.
+
+**[McCallum1998]** A. McCallum and K. Nigam, A comparison of event models for naive bayes text classification, AAAI-98 workshop on learning for text categorization, 1998.
+
+**[McCullagh1989]** Peter McCullagh and John Ashworth
+Nelder, Generalized Linear Models, Second Edition,
+Monographs on Statistics and Applied Probability, Number 37,
+1989, Chapman & Hall/CRC, 532.
+
+**[Nelder1972]** John Ashworth Nelder and Robert
+William Maclagan Wedderburn, Generalized Linear Models,
+Journal of the Royal Statistical Society, Series A
+(General), 1972, Volume 135, Number 3,
+370--384.
+
+**[Nocedal1999]** J. Nocedal and S. J. Wright, Numerical Optimization, Springer-Verlag, 1999.
+
+**[Nocedal2006]** Optimization Numerical Optimization,
+Jorge Nocedal and Stephen Wright, Springer Series
+in Operations Research and Financial Engineering, 664,
+Second Edition, Springer, 2006.
+
+**[PandaHBB2009]** B. Panda, J. Herbach, S. Basu, and R. J. Bayardo. PLANET: massively parallel learning of tree ensembles with mapreduce. PVLDB, 2(2):1426– 1437, 2009.
+
+**[Russell2009]** S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, 2009.
+
+**[Scholkopf1995]** B. Scholkopf, C. Burges and V. Vapnik, Extracting Support Data for a Given Task, International Conference on Knowledge Discovery and Data Mining (ICDM), 1995.
+
+**[Stevens1946]** Stanley Smith Stevens,
+On the Theory of Scales of Measurement, Science
+June 7, 1946, Volume 103, Number 2684,
+677--680.
+
+**[Vetterling1992]**
+W. T. Vetterling and B. P. Flannery,
+Multidimensions in Numerical Recipes in C - The Art in Scientific Computing, W. H. Press and S. A. Teukolsky (eds.), Cambridge University Press, 1992.
+
+**[ZhouWSP08]**
+Y. Zhou, D. M. Wilkinson, R. Schreiber, and R. Pan. Large-scale parallel collaborative filtering for the Netflix prize.
+In Algorithmic Aspects in Information and Management, 4th International Conference, AAIM 2008, Shanghai, China, June 23-25, 2008. Proceedings, pages 337–348, 2008.