This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real life questions, to choose and apply appropriate methods to answer those questions, and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, prediction with machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by over 360 practice questions and 120 data exercises.

Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, and generalization before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton-proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists maybe able to contribute. (Notebooks are available at https://physics.bu.edu/~pankajm/MLnotebooks.html )

The eight sections are (1) Artificial Intelligence (introductory material); (2) Problem-Solving (search and game playing); (3) Knowledge and Reasoning (propositional and predicate logic, inference techniques, knowledge representation); (4) Acting Logically (planning); (5) Uncertain Knowledge and Reasoning (probabilistic reasoning, Bayesian nets, decision-theoretic techniques); (6) Learning (inductive learning, neural nets, reinforcement learning); (7) Communicating, Perceiving, and Acting (natural language processing, computer vision, robotics); and (8) Conclusions (philosophical foundations and summary). What makes this textbook so good? First, it is remarkably comprehensive. In the preface, the authors suggest several alternative paths through the book that could serve as the basis of a one-semester course. At the University of Pittsburgh, my colleagues and I cover roughly the first half of the book (Sections 1-4) in the firstsemester introductory graduate AI course, covering most of Sections 5 through 8 in a second-semester course.

Your consumers spend a lot of time exploring and analyzing suitable information―which books to study, which news articles to read, which songs to play, which movies to watch, which games to play, and so on. Imagine, what their experience would be like, if they don't need to pick anything on their own, but are presented with options of their liking―be it in education or media or entertainment. Here are some of the things they can be offered: • Adaptive text-books, in which content changes based on the pace of learning and comfort level of the reader. Such advancements reduce the overall time spent on information discovery, and increase the scope of effective information consumption (or learning). Domains such as education, publishing, entertainment, and advertisement mostly deal with granular digital assets (text, images, audio, video, multi-media, and so on), and are better prepared to enhance personalization even without creating new content from scratch.

Your consumers spend a lot of time exploring and analyzing suitable information―which books to study, which news articles to read, which songs to play, which movies to watch, which games to play, and so on. Imagine, what their experience would be like, if they don't need to pick anything on their own, but are presented with options of their liking―be it in education or media or entertainment. Here are some of the things they can be offered: • Adaptive text-books, in which content changes based on the pace of learning and comfort level of the reader. Such advancements reduce the overall time spent on information discovery, and increase the scope of effective information consumption (or learning). Domains such as education, publishing, entertainment, and advertisement mostly deal with granular digital assets (text, images, audio, video, multi-media, and so on), and are better prepared to enhance personalization even without creating new content from scratch.

This book covers the topic of recommender systems comprehensively, starting with the fundamentals and then exploring the advanced topics. 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. Recommendations in specific domains and contexts: The context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed.