The Artificial Intelligence (AI) Journey presented by Stan Mlynarczyk
An overview of AI; its history, current state, challenges, successes and deeper focus on text analytics and Natural Language Processing (NLP). AI is a broad topic and some of the approaches (such as Machine Learning) will be discussed. Text Analytics and NLP (Amazon Echo Anyone?) impacting our every-day existence, but what is at the core of these solutions? Whether you have a casual interest in the topic or are looking to implement sentiment analysis or text classification, this presentation seeks to satisfy on both levels.
About the speaker: Stan Mlynarczyk is actively involved with research in AI/Text Analytics and Natural Language Processing (NLP). His Ph.D. is from DePaul University (2009), where he focused on Natural Language research and Artificial Intelligence. Currently a Professor at American University (Washington D.C.) teaching courses in Database and BigData, Stan has been an instructor for four years, teaching the full stack of Hadoop courses for HortonWorks and Cloudera (Analytics, DataScience, Spark, Hive, Pig, HBase, Administration) as an independent consultant and as an employee at Cloudera. Stan launched and led the Enterprise Architecture and BigData Hadoop organizations at Teradata (the world's leading data warehouse provider). Also while at Teradata, Stan created the High Availability offering that provides for fault tolerant operation of peta-byte level Teradata databases. Stan's current focus is research and development of tools for advanced text analytics and Natural Language Processing.
Machine Learning on Graph Databases: Motivation, Use Cases, Algorithms - presented by Ted Gifford, Schneider
Abstract: Graph Theory has been an integral part of mathematics for 250 years and of Computer Science theory from its beginning. Surprisingly, the notion of building large-scale commercial databases based on graph theory is very recent. Unsurprisingly, digital social networks and the internet are the catalysts that have driven this development. Some of the use cases that we will consider are recommendation systems, intelligence analysis (e.g., cyber terrorism), fraud detection, IT operations management, and Master Data Management. I will provide a brief review of (mathematical) graph theory and then describe the structure and attributes of graph databases along with some algorithms that can be executed over graph databases, including page-rank, centrality, clustering, search, and modularity.
About the Speaker: Ted Gifford is a Distinguished Engineer at Schneider National, where he previously served as Vice President of Engineering and Research. His current interests lie in the intersection of Data Science and Operations Research, particularly combining machine learning techniques with mathematical optimization. In previous roles he was the founder/president of a software engineering company and a professor of Computer Science at the University of Alaska. He is a past recipient of the Wagner Prize, the INFORMS Prize, and Innovative Applications in Analytics Award.