Vukosi works on developing Machine Learning/Artificial Intelligence methods to extract insights from data. ... Her master's work is about using satellite images and computer vision to study the effects and evolution of spatial apartheid in South Africa and she is a recipient of the Data Science for Social Good fellowship at the University of ...
GSF uses the full power of cloud and enterprise architecture and can quickly run automated analytics on existing data stores or new and incoming data. Available analytics include Harris' advanced machine learning capabilities, algorithms an organisation is already using, and any of the powerful analytics available within ENVI software such as ...
2 · Description: Learn about High-level overview of Data Science project management methodology, Statistical Analysis using examples, understand Statistics and Statistics 101. Also, learn about exploratory data analysis, data cleansing, data preparation, feature engineering. Topics. High-Level overview of Data Science / Machine Learning project management methodology
Data mining is the process of discovering meaningful patterns in large datasets to help guide an organization's decision-making. With the use of techniques like regression, classification, and cluster analysis, data mining can sort through vast amounts of raw data to analyze customer preferences, detect fraudulent transactions, or perform social network analyses.
The theoretical basis for the method was developed by the French mathematician Georges Matheron in 1960, based on the Master's thesis of Danie G. Krige, the pioneering plotter of distance-weighted average gold grades at the Witwatersrand reef complex in South Africa.Krige sought to estimate the most likely distribution of gold based on samples from a few boreholes.
Spatial Data Science Masters Program: Solving data-intensive, large-scale, location-based problems. Geospatial data accessibility, spatial decision support systems, and geospatial problem-solving environments are revolutionizing most industries and disciplines, including health care, marketing, social services, human security, education, environmental sustainability, and transportation.
A vast majority of the work presented at the three workshops so far have demonstrated the work being done in Africa that applies machine learning and data science to find solutions. The applications have been varied from agriculture, health, conservation and disaster management.
Post Doctoral Fellow, Machine Learning and Mathematical Analysis of Spatial Transcriptomics Data (GIS)
Duration: 36 months, Ideally commencing 1st November 20' . Applications are invited from enthusiastic and talented individuals for a 36-month post–doctorate research position which will contribute to the Horizon 2020 funded project SENATOR..
Post Doctoral Fellow, Machine Learning and Mathematical Analysis of Spatial Transcriptomics Data (GIS)
Spatial data mining is the application of data mining to spatial models. In spatial data mining, analysts use geographical or spatial information to produce business intelligence or other results. This requires specific techniques and resources to get the geographical data into relevant and useful formats.
My doctoral work aimed at rendering machine understandable the social dimension of urban places. The main outcome has been a formal framework, encoded into the form of ontology design patterns, to represent the architectural aspects of the city, its form, and the socio-spatial behaviour of city dwellers.
Jun 30, 2020· South Africa's Gauteng provincial government is using machine learning-powered technology to monitor, track and predict COVID-19 infections across the province. ... using machine-learning-powered spatial monitoring, ... AFRICA BY NUMBERS Crunch the data with stats from all 55 countries. PORTRAIT GALLERY.
Jul 28, 2020· Some prominent big data technologies like Machine learning, Apache Storm; Hadoop and Map Reduce technology, distributed file system with Hive and HBase, data mining tools like RapidMiner, Weka, Orange, Knime or data visualization tools like R, Python, Hadoop, Appache storm, NO SQL, Neo4j, Cassendra etc have been incorporated in the core course ...
African students and researchers are called to participate in the First Africa Summer School on Machine Learning for Data Mining and Search, sponsored by ACM SIGIR and SIGKDD, hosted at the University of Cape Town, and presented by leading international researchers, from 14 -18 January 2019.
Apr 22, 2020· All AI and machine learning (ML) applications on Earth observation require quality ground reference data, which are accurate observations of features on the ground to use as a label of what an overhead image (e.g., satellite remote sensing data) represents.This is especially true for applications in agriculture. Inconsistent, mislabeled, or inaccurately collected ground reference data in …
In this study, we develop a data-driven framework that integrates machine learning with spatial statistics, and then use it on Xiamen Island, China to delineate urban population dynamic patterns based on hourly Baidu heat map data collected from August 25 to September 3, 2017.
spatial modeling of human and biophysical systems, drawing on large amounts of data, and using a variety of machine learning and data-mining ... and using a variety of machine learning and data-mining ... activities focused on sub-Saharan Africa. This post-doctoral fellow will work as a member of CIMMYT's Socio-Economics Program in close ...
Jan 31, 2020· Internationally recognized for his work in Machine Learning and Artificial Intelligence, author and co-author of more than 300 refereed papers, and a supervisor of more than 70 graduate students. Stan has taught Machine Learning, Data mining and Big Data at universities in Canada, the U.S, Europe, and Latin America.
Jul 15, 2020· Statistical tools from data mining, predictive modeling, and machine learning analyze pattern in historical data, to make predictions about future events as well as intelligent actions.