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These projects involve the development of machine learning algorithms to extend the existing ECL-ML (ECL Machine Learning) on the HPCC Systems platform, using ECL and the underlying Parallel Block BLAS (PB-BLAS) infrastructure.
While the general purpose is to develop these algorithms mostly in ECL, using other languages that can be embedded in HPCC (Python, Java, Javascript, R, C++) is an acceptable option too. A few functions are C++ functions with a C++/ECL wrapper around them.
The HPCC Systems Machine Learning Library, is a work in progress. However, it already contains many classification, regression and clustering algorithms.
There are currently 2 available projects in this area:
Objectives for the ML functions
- To be able to handle large training sets in a timely manner by distributing the training set across the nodes of an HPCC System Thor cluster.
- To produce statistics that measure the goodness of the fit of models created by the ML functions
Available Resources
- Examples of existing code
- HPCC Systems Machine Learning documentation
Machine learning projects previously completed by students
- Implement a Gradient Trees Algorithm
In progress. To be completed by George Mathew (NCSU PhD student in Computer Science) as part of the 2017 HPCC Systems Intern Program - Documentation Generator for ECL Code
In progress. To be completed by Sarthak Jain (Northwestern University PhD student in Computer Science) as part of the 2017 HPCC Systems Intern Program - Implement a Latent Semantic Analysis Algorithm in ECL
Completed as part of the HPCC Systems Summer Internship Program 2016 - Empower ECL-ML: How to make the HPCC Systems Machine Learning Library easier to use
Completed as part of the HPCC Systems Summer Internship Program 2016 - Implement a YinYang K-Means Clustering Algorithm in ECL
Completed as part of the HPCC Systems Summer Internship Program 2016 - Implement the Converse Sparse Cholesky Selection Algorithm in ECL
Completed as part of the HPCC Systems Summer Internship Program 2016 - Analyse which algorithms may provide the best results for the implementation of Non-Negative Matrix Factorisations (NMF) in ECL
Completed as an independent voluntary contribution in 2016 - Add new statistics to the Linear and Logistic Regression Modules
Completed as part of the GSoC Program 2015 - Implement the CONCORD Algorithm
Completed as part of the HPCC Systems Summer Internship Program 2015