Graph-based machine learning is becoming an important trend in Artificial Intelligence, transcending a lot of other techniques.
Using graphs as basic representation of data for ML purposes has several advantages: (i) the data is already modeled for further analysis, explicitly representing connections and relationships between things and concepts; (ii) graphs can easily combine multiple sources into a single graph representation and learn over them, creating Knowledge Graphs; (iii) improving computation performances and quality. The talk will discuss these advantages and present applications in the context of recommendation engines and natural language processing.
YOU MAY ALSO LIKE:
- Brian Sletten's Data Science with R Workshop (in London on 5th - 7th December 2018)
- Fast Track to Machine Learning with Louis Dorard (in London on 11th - 13th December 2018)
- Deep Learning Fundamentals (in London on 25th - 26th March 2019)
- Infiniteconf 2019 - The conference on Big Data and AI (in London on 4th - 5th July 2019)
Graph-Powered Machine Learning
Alessandro has been a long-time member of the graph community and he is the main author of the first-ever recommendation engine based on Neo4j. At GraphAware, he specialises in recommendation engines, graph-aided search, and NLP. He has recently built an application using Neo4j and Elasticsearch aimed at personalising search results, utilizing several machine learning algorithms, natural language processing and ontology hierarchy. Before joining the team, Alessandro has gained over 10 years of experience in software development and spoke at many prominent conferences, such as JavaOne. Alessandro holds a Ph.D. in Computer Science from University of Salento.