Dr. Jian Pei is a Professor in the School of Computing Science at Simon Fraser University. He is a well-known leading researcher in the general areas of data science, big data, data mining, and database systems. His expertise is on developing effective and efficient data analysis techniques for novel data intensive applications, and transferring his research results to products and business practice. He is recognized as a Fellow of the Royal Society of Canada (Canada's national academy), the Canadian Academy of Engineering, the Association of Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE). He is one of the most cited authors in data mining, database systems, and information retrieval.

Dr. Guilin Qi is a professor at Southeast University, China, where he also serves as director of the Institute of Cognitive Intelligence and of the Knowledge Science and Engineering Lab. His research interests include knowledge representation and reasoning, knowledge graphs, uncertainty reasoning, and the semantic web. Prof. Qi is an editorial board member of the Journal of Web Semantics, and has co-edited special issues for the Annals of Mathematics and Artificial Intelligence, International Journal of Approximate Reasoning and Journal of Applied Logic. He has over 20 years of research experiences in knowledge engineering and has led many national and industrial projects on knowledge graphs. Prof. Qi has published more than 100 papers on knowledge engineering and knowledge graphs and holds two patents.

Developing Enterprise Chatbots: Learning Linguistic Structures [Galitsky, Boris] on Amazon.com. *FREE* shipping on qualifying offers. Developing Enterprise Chatbots: Learning Linguistic Structures

Customer satisfaction is a potent metric that directly influences the profitability of an organization. Establishing highly efficient contact centers requires significant automation, the ability to scale, and a mechanism of active learning through customer feedback. There is a challenge at every point in the contact center customer journey--from long hold times at the beginning to operational costs associated with long average handle times. In traditional contact centers, one solution for long hold times is enabling self-service options for customers using an Interactive Voice Response system (IVR). An IVR uses a set of automated menu options to help reduce agent call volumes by addressing common frequently asked requests without involving a live agent.

Computational Learning Theory: Third European Conference, EuroCOLT '97, Jerusalem, Israel, March 17 - 19, 1997, Proceedings (Lecture Notes in Computer Science, 1208) [Ben-David, Shai] on Amazon.com. *FREE* shipping on qualifying offers. Computational Learning Theory: Third European Conference, EuroCOLT '97, Jerusalem, Israel, March 17 - 19, 1997, Proceedings (Lecture Notes in Computer Science, 1208)

The production, shipping, usage, and disposal of consumer goods have a substantial impact on greenhouse gas emissions and the depletion of resources. Modern retail platforms rely heavily on Machine Learning (ML) for their search and recommender systems. Thus, ML can potentially support efforts towards more sustainable consumption patterns, for example, by accounting for sustainability aspects in product search or recommendations. However, leveraging ML potential for reaching sustainability goals requires data on sustainability. Unfortunately, no open and publicly available database integrates sustainability information on a product-by-product basis. In this work, we present the GreenDB, which fills this gap. Based on search logs of millions of users, we prioritize which products users care about most. The GreenDB schema extends the well-known schema.org Product definition and can be readily integrated into existing product catalogs to improve sustainability information available for search and recommendation experiences. We present our proof of concept implementation of a scraping system that creates the GreenDB dataset.

Robotics: Modelling, Planning and Control (Advanced Textbooks in Control and Signal Processing) [Siciliano, Bruno, Sciavicco, Lorenzo, Villani, Luigi, Oriolo, Giuseppe] on Amazon.com. *FREE* shipping on qualifying offers. Robotics: Modelling, Planning and Control (Advanced Textbooks in Control and Signal Processing)

As enterprises move from running ad hoc machine learning (ML) models to using AI/ML to transform their business at scale, the adoption of ML Operations (MLOps) becomes inevitable. As shown in the following figure, the ML lifecycle begins with framing a business problem as an ML use case followed by a series of phases, including data preparation, feature engineering, model building, deployment, continuous monitoring, and retraining. For many enterprises, a lot of these steps are still manual and loosely integrated with each other. Therefore, it's important to automate the end-to-end ML lifecycle, which enables frequent experiments to drive better business outcomes. Data preparation is one of the crucial steps in this lifecycle, because the ML model's accuracy depends on the quality of the training dataset.

Computer Vision: Algorithms and Applications (Texts in Computer Science) [Szeliski, Richard] on Amazon.com. *FREE* shipping on qualifying offers. Computer Vision: Algorithms and Applications (Texts in Computer Science)

Warren B. Powell is Professor Emeritus at Princeton University, where he taught for 39 years, and is currently Chief Analytics Officer at Optimal Dynamics. He is the founder and director of CASTLE Labs, which developed models and algorithms in stochastic optimization, with applications to energy systems, transportation, health, e-commerce, and the laboratory sciences (see www.castlelab.princeton.edu). He has pioneered the use of approximate dynamic programming for high-dimensional applications, and the knowledge gradient for active learning problems. His recent work has focused on developing a unified framework for sequential decision problems under uncertainty, spanning active learning to a wide range of dynamic resource allocation problems. He has authored books on Approximate Dynamic Programming and (with Ilya Ryzhov) Optimal Learning, and is the author of Reinforcement Learning and Stochastic Optimization: A unified framework for sequential decisions.