“The development of full artificial intelligence could spell the end of the human race. It would take off on its own, and re- design itself at an ever increasing rate. Humans, who are limited by slow biological evolution, couldn’t compete, and would be superseded.” – Stephen Hawking
More and more business organizations are using A.I. technologies such as predictive analytics, deep learning or sentiment / image analysis to identify patterns and trends in vast reams of (big) data, allowing them to make ‘smarter’ decisions (e.g. about loss of customers or the necessary service inspection of equipment) and potentially to become more competitive in real-time. As A.I. technology is already surpassing human decision- making in certain instances, there is growing concern about ‘uncontrolled A.I.’ in business and society, incl. regulatory and ethical-legal challenges. Against this background, this new course aims to equip students with foundational, theoretical and practical knowledge about A.I. driven business applications in selected private and public sector organizations. At the beginning of the course, students will watch Ex-Machina (2015), a psycho- techno thriller film which tells the story of a young programmer invited to the home of a reclusive billionaire to test whether an A.I. can pass itself off as human to assess the difficulties to successfully control and ‘switch- off’ artificial intelligence once created. Besides reconstructing the history of artificial intelligence from the 1960s to the current era (as well as a refresher about the basics of computer science algorithms such as audio and video compression algorithms), we will put emphasis on explaining the A.I. driven business models of several top international and local organizations such as New Relic, Splunk, Trade Desk, Nvidia, MetaMind, DBS, NTUC, Gov Tech Agency of Singapore and the German Research Center for A.I. In order to appreciate the power of A.I. technology, we will take a closer look ‘under the A.I. hood’ to understand what makes machine learning, deep learning, neural networks and image analysis tick. Visits to A.I. powered business organizations engaged in customer service management, finance, marketing, supply chain management or manufacturing will be organised aimed at appreciating both the benefits and downside of A.I. Students graduating from this course will be equipped with critical competencies to solve real-world business problems using A.I. technology while simultaneously casting a critical eye on the morality and ethics of commercialising A.I.
By the end of this course, students will be able to:
The business model book : design, build and adapt business ideas that thrive by
Business models are the beating heart of your firm's value proposition. Great business models drive rapid growth; bad business models can doom the most promising ventures. The Business Model Book clearly shows you how to create, test, adapt and innovate successful business models for any company in any situation. Every company has a business model. When the business model works, the company creates value. The Business Model Book combines the latest research, straightforward tools and current examples to bring this surprisingly tricky topic to life. Your company changes over time, and so should your business model. · Understand how and why business models matter to your organisations success. · Be able to apply business model thinking to a specific company and a specific situation. · Learn how to evaluate and test business models to identify the most appropriate model. · Use a set of practical tools to adapt and innovate business models. It does not matter what you think you know about business models. It is time to design, build and adapt business ideas that thrive. " Clever, innovative, and simple -- a must read workbook for entrepreneurs!" Charles CHEN Yidan, Co-Founder, Tencent Holdings. "Buy it. Read it. Most importantly, use it!" John Mullins, London Business School, Author of The Customer-Funded Business and The New Business Road Test. "This may well turn out to be your scrapbook for business models. An excellent resource that will get your ideas flowing!" Shane Corstorphine, VP, Regional Growth (Global Regions) and General Manager (Americas), Skyscanner.
Artificial Intelligence in the 21st Century by
This new edition provides a comprehensive, colorful, up-to-date, and accessible presentation of AI without sacrificing theoretical foundations. It includes numerous examples, applications, full color images, and human interest boxes to enhance student interest. New chapters on robotics and machine learning are now included. Advanced topics cover neural nets, genetic algorithms, natural language processing, planning, and complex board games. A companion DVD is provided with resources, applications, and figures from the book. Numerous instructors' resources are available upon adoption. eBook Customers:Companion files are available for downloading with order number/proof of purchase by writing to the publisher at info@merclearning.com. FEATURES: * Includes new chapters on robotics and machine learning and new sections on speech understanding and metaphor in NLP * Provides a comprehensive, colorful, up to date, and accessible presentation of AI without sacrificing theoretical foundations * Uses numerous examples, applications, full color images, and human interest boxes to enhance student interest * Introduces important AI concepts e.g., robotics, use in video games, neural nets, machine learning, and more thorough practical applications * Features over 300 figures and color images with worked problems detailing AI methods and solutions to selected exercises * Includes DVD with resources, simulations, and figures from the book * Provides numerous instructors' resources, including: solutions to exercises, Microsoft PP slides, etc.
Machine Learning by
A concise overview of machine learning--computer programs that learn from data--which underlies applications that include recommendation systems, face recognition, and driverless cars. Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognition--as well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as "Big Data" has gotten bigger, the theory of machine learning--the foundation of efforts to process that data into knowledge--has also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications. Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of "data science," and discusses the ethical and legal implications for data privacy and security.
Introduction to Machine Learning by
A substantially revised third edition of a comprehensive textbook that covers a broad range of topics not often included in introductory texts. The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. Subjects include supervised learning; Bayesian decision theory; parametric, semi-parametric, and nonparametric methods; multivariate analysis; hidden Markov models; reinforcement learning; kernel machines; graphical models; Bayesian estimation; and statistical testing. Machine learning is rapidly becoming a skill that computer science students must master before graduation. The third edition of Introduction to Machine Learning reflects this shift, with added support for beginners, including selected solutions for exercises and additional example data sets (with code available online). Other substantial changes include discussions of outlier detection; ranking algorithms for perceptrons and support vector machines; matrix decomposition and spectral methods; distance estimation; new kernel algorithms; deep learning in multilayered perceptrons; and the nonparametric approach to Bayesian methods. All learning algorithms are explained so that students can easily move from the equations in the book to a computer program. The book can be used by both advanced undergraduates and graduate students. It will also be of interest to professionals who are concerned with the application of machine learning methods.
Big Data, Data Mining, and Machine Learning by
With big data analytics comes big insights intoprofitability Big data is big business. But having the data and thecomputational power to process it isn't nearly enough to producemeaningful results. Big Data, Data Mining, and Machine Learning:Value Creation for Business Leaders and Practitioners is acomplete resource for technology and marketing executives lookingto cut through the hype and produce real results that hit thebottom line. Providing an engaging, thorough overview of thecurrent state of big data analytics and the growing trend towardhigh performance computing architectures, the book is adetail-driven look into how big data analytics can be leveraged tofoster positive change and drive efficiency. With continued exponential growth in data and ever morecompetitive markets, businesses must adapt quickly to gain everycompetitive advantage available. Big data analytics can serve asthe linchpin for initiatives that drive business, but only if theunderlying technology and analysis is fully understood andappreciated by engaged stakeholders. This book provides a view intothe topic that executives, managers, and practitioners require, andincludes: A complete overview of big data and its notablecharacteristics Details on high performance computing architectures foranalytics, massively parallel processing (MPP), and in-memorydatabases Comprehensive coverage of data mining, text analytics, andmachine learning algorithms A discussion of explanatory and predictive modeling, and howthey can be applied to decision-making processes Big Data, Data Mining, and Machine Learning providestechnology and marketing executives with the complete resource thathas been notably absent from the veritable libraries of publishedbooks on the topic. Take control of your organization's big dataanalytics to produce real results with a resource that iscomprehensive in scope and light on hyperbole.
Machine Learning and Cognition in Enterprises Business Intelligence Transformed. Rohit. Kumar
A Practical Guide to Data Mining for Business and Industry by
Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest.