Math Professor Sophie Knowles finds herself in the middle of an investigation after her friend, Henley College librarian, Charlotte Crocker, is found dead in the stacks. When Sophie learns that Charlotte wasn't the quiet, law-abiding citizen she seemed to be, she sets out to determine who her friend really was, and to find her killer. As she deals with a group of lottery players, winners and losers, the odds are Sophie will solve the puzzle.
Dr. Sophie Knowles is a professor with a way of making even the most complex math problems fun for her students. But when the school's beloved librarian is found shot to death in the stacks, Sophie learns that her friend was more complex than she ever knew. Now, Sophie must take on some rigorous deduction homework before the chances for another murder on campus increase exponentially...
Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem. This book tackles this challenge through model-based machine learning which focuses on understanding the assumptions encoded in a machine learning system and their corresponding impact on the behaviour of the system. The key ideas of model-based machine learning are introduced through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter introduces one case study and works through step-by-step to solve it using a model-based approach. The aim is not just to explain machine learning methods, but also showcase how to create, debug, and evolve them to solve a problem. Features: Explores the assumptions being made by machine learning systems and the effect these assumptions have when the system is applied to concrete problems. Explains machine learning concepts as they arise in real-world case studies. Shows how to diagnose, understand and address problems with machine learning systems. Full source code available, allowing models and results to be reproduced and explored. Includes optional deep-dive sections with more mathematical details on inference algorithms for the interested reader.
Dr. Sophie Knowles is a math professor with a knack for creating complex puzzles that delight her students. But now, at the close of the academic year, she must solve a crime that doesn’t quite add up… At the math department’s graduation party, Sophie hears heated arguments coming from the graduates about Mayor Graves, the commencement speaker. Not the mayor’s biggest fan, Sophie is happy to escape the drama with an after-hours campus stroll accompanied by her helicopter-piloting boyfriend, Bruce Granville. However, their date is interrupted by the mayor himself—with a knife in his back. As it turns out, the knife is actually a Henley College letter opener—something that is gifted to every member of the graduating class. Sophie is led to a complicated puzzle of scandal and corruption, and it seems that Mayor Graves is at the apex of it all. When Sophie finds out that the mayor was seeking her help on the day he was murdered, she must use her top-notch logic to crack the puzzle and catch the killer running free on campus…
Dr. Sophie Knowles teaches math at Henley College in Massachusetts, but when a colleague turns up dead, it's up to her to find the killer before someone else gets subtracted.
Much of our thinking is flawed because it is based on faulty intuition. By using the framework and tools of probability and statistics, we can overcome this to provide solutions to many real-world problems and paradoxes. We show how to do this, and find answers that are frequently very contrary to what we might expect. Along the way, we venture into diverse realms and thought experiments which challenge the way that we see the world. Features: An insightful and engaging discussion of some of the key ideas of probabilistic and statistical thinking Many classic and novel problems, paradoxes, and puzzles An exploration of some of the big questions involving the use of choice and reason in an uncertain world The application of probability, statistics, and Bayesian methods to a wide range of subjects, including economics, finance, law, and medicine Exercises, references, and links for those wishing to cross-reference or to probe further Solutions to exercises at the end of the book This book should serve as an invaluable and fascinating resource for university, college, and high school students who wish to extend their reading, as well as for teachers and lecturers who want to liven up their courses while retaining academic rigour. It will also appeal to anyone who wishes to develop skills with numbers or has an interest in the many statistical and other paradoxes that permeate our lives. Indeed, anyone studying the sciences, social sciences, or humanities on a formal or informal basis will enjoy and benefit from this book.
Dr. Sophie Knowles loves using puzzles to make math fun for students. But when winter seizes Henley College, she must thaw out a cold case to track down a killer--her most difficult puzzle yet . . . Winter Intersession is in full swing, and campus is buzzing over the concert celebrating the bell tower's reopening. The building has been shuttered for twenty-five years, and Sophie's shocked to learn why--a student leapt from it to her death. But she's even more troubled by the secrecy surrounding the case. After Sophie performs some quick calculations, she's left with a nagging question: Was it really suicide? When one of Sophie's favorite students, a performer in the concert, is brutally beaten and left in a coma, Sophie's mind kicks into overdrive. The horrific incidents seem too coincidental to be unrelated, but can Sophie put together the pieces from a twenty-five-year-old murder before any other students get hurt?
Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and toolsfor understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment. The second edition adds many new examples, exercises, and explanations, to deepen understanding of the ideas, clarify subtle concepts, and respond to feedback from many students and readers. New supplementary online resources have been developed, including animations and interactive visualizations, and the book has been updated to dovetail with these resources. Supplementary material is available on Joseph Blitzstein’s website www. stat110.net. The supplements include: Solutions to selected exercises Additional practice problems Handouts including review material and sample exams Animations and interactive visualizations created in connection with the edX online version of Stat 110. Links to lecture videos available on ITunes U and YouTube There is also a complete instructor's solutions manual available to instructors who require the book for a course.
