To ensure that readers fully understand the topic and its applications, the authors provide motivating examples throughout. AI in Practice boxes appear in each chapter, demonstrating real-world uses of artificial intelligence by NASA, General Motors Corporation, Microsoft Corporation, and other companies. LISP Implementation appendices are found at the end of most chapters, providing fully-documented implementations of important algorithms. Plus, the text features summaries, exercises, and background sections describing related work at the end of each chapter.
Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. In addition, recent topics, such as multi-armed bandits, learning to rank, group systems, multi-criteria systems, and active learning systems, are discussed together with applications. For subscribing institutions click from a computer directly connected to your institution network to download the book for free. To be eligible, your institution must subscribe to "e-book package English (Computer Science)" or "e-book package English (full collection)".
Apache Spark continues to gain momentum in today's big data analytics landscape. The major topics include Spark Components, Common Spark Algorithms-Iterative Algorithms, Graph Analysis, Machine Learning, Running Spark on a Cluster. Since Spark has the ability to run on diverse platforms using various languages, it is an important phase to gain insights into developing application with various mentioned programming languages. This learning package also covers Spark, Hadoop, and the Enterprise Data Centre, Common Spark Algorithms and Spark Streaming, which is yet another important feature of Spark.