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Bart Baesens
Приєднався 24 вер 2013
Videos related to analytics, database management, data mining, social networks
Fundamentals in Climate Risk & Sustainability Management: Physical Risk Data & Assessment
Demo lecture of our BlueCourses Fundamentals in Climate Risk & Sustainability Management course.
For more details, see www.bluecourses.com/courses/course-v1:bluecourses+BC19+2021_Q1/about
For more details, see www.bluecourses.com/courses/course-v1:bluecourses+BC19+2021_Q1/about
Переглядів: 146
Відео
Gaining T(h)rust with AI - Guest Lecture for Odesa I.I. Mechnikov National University
Переглядів 1713 місяці тому
In this guest lecture for Odesa I.I. Mechnikov National University, I will elaborate on some recent developments in generative AI. We start by refreshing large language models (e.g., ChatGPT, BERT), the underlying transformer architecture (encoder/decoder setup) and attention mechanism. We discuss the economic, job and education impact of these technologies. Next, we zoom in on AGI/Singularity ...
Gaining T(h)rust with AI
Переглядів 2544 місяці тому
In this talk, I will elaborate on some recent developments in generative AI. We start by refreshing large language models (e.g., ChatGPT, BERT), the underlying transformer architecture (encoder/decoder setup) and attention mechanism. We discuss the economic, job and education impact of these technologies. Next, we zoom in on AGI/Singularity and review key criteria for trustworthy AI such as ope...
Royal Navy
Переглядів 2446 місяців тому
For paper, see ieeexplore.ieee.org/document/10371273?fbclid=IwAR2N2ZuKa9kZirFwgbWWULiEAffuuHhMaCLpfv5wIzzFsCthvMk3TlFZhVQ We research how deep learning convolutional neural networks can be used to to automatically classify the unique data set of black-and-white naval ships images from the Wright and Logan photographic collection held by the National Museum of the Royal Navy. We contrast various...
Bitcoin, Crytptocurrencies and Blockchain explained.
Переглядів 9446 місяців тому
In this on-line lecture, dr. Almer Gungor (www.linkedin.com/in/almer-gungor/) explains the basics of Bitcoin, cryptocurrencies and blockchain.
Model Risk
Переглядів 3959 місяців тому
This presentation is about all the types of model risk that can occur for analytical models, as even though the interest in using machine learning, deep learning, and artificial intelligence continues to rise, we feel that many organizations have also observed (or underwent, in some cases) that these techniques also come with potential risks in how they're being constructed, implemented and use...
Research Collaboration Bart Baesens/Seppe vanden Broucke - National Museum of the Royal Navy
Переглядів 1439 місяців тому
Research Collaboration Bart Baesens/Seppe vanden Broucke - National Museum of the Royal Navy
Introduction to Analytics
Переглядів 3569 місяців тому
Many companies are flooded with huge amounts of data available in corporate databases. A key challenge is how to optimally manage this data overload and use analytics to better understand, manage, and strategically exploit the complex dynamics of customer behavior. This lecture starts by giving an overview of the steps involved when working out an analytics project in a practical business setti...
Boosting Credit Risk Models
Переглядів 1,9 тис.9 місяців тому
For the paper, see papers.ssrn.com/sol3/papers.cfm?abstract_id=4523688 For my Credit Risk courses, see www.bluecourses.com. In this video, we give various recommendations to boost the performance of credit risk models. It is based upon more than two decades of research and consulting on the topic. Building credit risk models typically entails four steps: gathering and preprocessing data, modell...
Prof Bart BIEF screen record 2023 06 10
Переглядів 341Рік тому
Fraud is as old as humankind and appears in many types and forms. Popular examples are credit card fraud, tax evasion, identity theft, insurance fraud, counterfeit, click fraud, anti-money laundering, and payment transaction fraud. In earlier research we defined fraud as an uncommon, well-considered, imperceptibly concealed, time-evolving, and carefully organized crime. Nowadays, fraud is typic...
Social Networks for Fraud Detection - University of Nottingham Malaysia Campus
Переглядів 426Рік тому
Fraud is as old as humankind and appears in many types and forms. Popular examples are credit card fraud, tax evasion, insurance fraud, anti-money laundering, and payment transaction fraud. In earlier research we defined fraud as an uncommon, well-considered, imperceptibly concealed, time-evolving, and carefully organized crime. Nowadays, fraud is typically tackled using state-of-the-art predic...
Challenges in Machine Learning ( Odesa I.I. Mechnikov National University)
Переглядів 229Рік тому
Challenges in Machine Learning ( Odesa I.I. Mechnikov National University)
Boosting Credit Risk Models by Prof. Bart Baesens
Переглядів 3,4 тис.Рік тому
In this talk we elaborate on how to boost Credit Risk Models based upon more than 2 decades of research and consulting in the field. We elaborate on Credit Risk Model Requirements, Alternative Data Sources, Feature Engineering, Deep Learning and Profit Driven Modeling.
5 Business Intelligence and Analytics Part D
Переглядів 3,2 тис.2 роки тому
5 Business Intelligence and Analytics Part D
5 Business Intelligence and Analytics Part C
Переглядів 3,6 тис.2 роки тому
5 Business Intelligence and Analytics Part C
5 Business Intelligence and Analytics Part B
Переглядів 3,6 тис.2 роки тому
5 Business Intelligence and Analytics Part B
5 Business Intelligence and Analytics Part A
Переглядів 4 тис.2 роки тому
5 Business Intelligence and Analytics Part A
1 Business Information Systems, Strategy and Governance part C
Переглядів 5 тис.2 роки тому
1 Business Information Systems, Strategy and Governance part C
1 Business Information Systems, Strategy and Governance part B
Переглядів 6 тис.3 роки тому
1 Business Information Systems, Strategy and Governance part B
1 Business Information Systems, Strategy and Governance part A
Переглядів 10 тис.3 роки тому
1 Business Information Systems, Strategy and Governance part A
Thanks for the lesson @Bart Baesens XD
Very interesting, but I would have wished for more in-depth coverage of "resources" and their interaction with nodes. Resources after all are not actor-less, but are always under the control of an actor, a node. A black diamond, for example, is under the control of an organized group. Diamonds or mines thereof are under the control of the state, an actor/node. Except for air, resources are not node-less.
This is an amazing session. Found it in 2024 and happy to use the lessons in my day to day work.
Thx, appreciated!
Is the book free?
Can someone please upload the slides presented in this presentation?
Thanks Bart, really helpful👍
Hello Bart, from Belgium as well and I more than interested in the topics. Are the slides of the presentation available anywhere? Thanks in advance!
Waiting for my final round of interview for fraud investigator. Thank you for this video🙏
Good luck!
Good lecture on customer churn 👍 Company vs. individual perspective Low/no usage vs. cancellation Class skew ROC curve and AUC
Thx, Chris!
Hi! Could you please share where can I access the principle of database management videos in English? Thank you
sure, see www.pdbmbook.com
Now do wage fraud!
pdf file
Hi Bart, Your video on Fraud was very interesting and informative. I loved the way you explained in a very simple manner so I'm planning to go for a Fraud Analytics course. Looking forward to getting details from you. Course duration and Fee structure. Regards, Hemant
Thx, appreciated!
thanks
You're welcome!
You are a fluent reader!
Very interesting stuff!
Glad you enjoyed it
Thanks for sharing , using this lecture to prepare for my interview From South Africa
Good luck!
For more information on my courses on Credit Risk, Fraud Analytics, Machine Learning, Deep Learning, Web Scraping, see www.bluecourses.com
Thank you for your teachings, Professor Bart Baesens. They've been very impactful to my information management program. Warm regards.🙏
Thx, man, appreciated!
Terima kasih untuk materinya sangat bermanfaat
thanks for sharing!
very informative course, thank you
Thx, so much, are you talking about my BlueCourses course? (www.bluecourses.com) Bart.
This doesn't work for financial statements fraud detection sadly. Imbalance and no homogeneity and several other issues. But this was an real enlightenment. Thank you!
I'm from India interested in fraud analytics full course , do you offer a discount considering the region?
please mail me on Bart.Baesens@gmail.com
just wanted to comment on a possible mispronunciation @20:10. An ego in an ego-net is surrounded by alters. It sounds like she said elders. The auto captions got it wrong too.
Thx for noting Noah!
this was helpful. thank you!
thanks Ayanda, if you want some courses on the topic, see www.bluecourses.com
Very useful! thanks
For my Advanced Credit Risk Modeling for Basel/IFRS 9 using R/Python/SAS ON-LINE course, see www.bluecourses.com/courses/course-v1:bluecourses+BC2+September2019/about
For my Basic Credit Risk Modeling for Basel/IFRS 9 using R/Python/SAS ON-LINE course, see www.bluecourses.com/courses/course-v1:bluecourses+BC1+September2019/about
Fore more information on Machine Learning, Analytics, Data Science, see www.bluecourses.com
Fore more information on Machine Learning, Analytics, Data Science, see www.bluecourses.com
Fore more information on Machine Learning, Analytics, Data Science, see www.bluecourses.com
Fore more information on Machine Learning, Analytics, Data Science, see www.bluecourses.com
Fore more information on Machine Learning, Analytics, Data Science, see www.bluecourses.com
Fore more information on Machine Learning, Analytics, Data Science, see www.bluecourses.com
Fore more information on Machine Learning, Analytics, Data Science, see www.bluecourses.com
Fore more information on Machine Learning, Analytics, Data Science, see www.bluecourses.com
Fore more information on Machine Learning, Analytics, Data Science, see www.bluecourses.com
Fore more information on Machine Learning, Analytics, Data Science, see www.bluecourses.com
Fore more information on Machine Learning, Analytics, Data Science, see www.bluecourses.com
Very good! Thanks
Thank you too!
You can find here a presentation with a real use case on how you can predict customer churn for beverage machines ua-cam.com/video/X1Zjl7p2ipo/v-deo.html
is there continuati9on of this lecture? whole series?
Thank you Buddy
for more information, see www.bluecourses.com
For more information, see our ON-LINE courses on www.bluecourses.com