On November 15th, my credit risk analytics course will be available as e-Learning. Send me an email at Bart.Baesens@gmail.com Bart Baesens holds a master's degree in Business Engineering (option: Management Informatics) and a PhD in Applied Economic Sciences from KU Leuven University (Belgium). He is currently an associate professor at KU Leuven, and a guest lecturer at the University of Southampton (United Kingdom). He has done extensive research on data mining and its applications.
Credit risk analytics in R will enable you to build credit risk models from start to finish. Accessing real credit data via the accompanying website www.creditriskanalytics.net, you will master a wide range of applications, including building your own PD, LGD and EAD models as well as mastering industry challenges such as reject inference, low default portfolio risk modeling, model validation and stress testing. This book has been written as a companion to Baesens, B., Roesch, D. and Scheule H., Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS, John Wiley & Sons, 2016. Bart Baesens is a professor of Big Data and Analytics at KU Leuven (Belgium) and a lecturer at the University of Southampton (United Kingdom). He has written more than 200 scientific papers and 10 books.
Recently published by Cambridge University Press, Re-Engineering Humanity explores how artificial intelligence, automated decisionmaking, the increasing use of Big Data are shaping the future of humanity. This excellent interdisciplinary book is co-authored by Professors Evan Selinger and Brett Frischmann, and it critically examines three interrelated questions. Under what circumstances can using technology make us more like simple machines than actualized human beings? Why does the diminution of our human potential matter? What will it take to build a high-tech future that human beings can flourish in?
What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.
It's been almost 15 years since we saw the future of crime prevention in "Minority Report" – but today, we are beginning to see those then fictitious yet fantastical methods of predicting and preventing crime being implemented in various parts of the world. I'll briefly mention three examples below of how analytics is already being used to prevent crime today before going into more detail on a fourth example: using analytics to prevent a criminal from re-offending. In Los Angeles and in England "predictive policing" ("PrePol") has been deployed for over two decades. It has of course evolved over this time. Today PrePol utilises algorithms to identify crime "hot-spots".