Overview
In this session, we illustrate how practitioners in many fields rather than only computer scientists can employ Bayesian Networks as a very practical form of Artificial Intelligence (AI) for exploring complex problems. We present the remarkably simple theory behind Bayesian Networks and then demonstrate how to utilise them for research and analytics tasks. More specifically, we explain how supervised and unsupervised machine learning algorithms can perform knowledge discovery with Bayesian Networks in high-dimensional domains, e.g. financial markets.
Also, while AI is commonly associated with another buzzword, "Big Data", we show that Bayesian Networks can bring AI to problems for which we possess little or no data, such as "black swan" events in financial markets. Here, expert knowledge modelling is critical, and we describe how even a minimal amount of expertise can serve as a basis for robust reasoning under uncertainty with Bayesian Networks.
"Currently, Bayesian Networks have become one of the most complete, self-sustained and coherent formalisms used for knowledge acquisition, representation and application through computer systems." (Bouhamed et al., 2015)
Session Overview:
- The motivation for Bayesian Networks:
- The promise, the peril, and the limitations of AI
- Human cognitive limitations and biases in reasoning
- Background: A conceptual map of analytic modeling and reasoning
- Inference type: Probabilistic vs Deterministic
- Model purpose: Observational vs Causal Inference
- Model source: Data vs Theory
- The Bayesian Network paradigm as a unifying framework
- AI in practice:
- Expert knowledge modelling and reasoning under uncertainty
- Supervised and unsupervised machine learning for knowledge discovery
- Applications for risk management in the fintech space
Date:19 March 2019
Time: 4:30pm 6:30pm
Venue: 32 Carpenter Street, Singapore 059911
Speaker:
- Stefan Conrady, Managing Partner, Bayesia USA & Singapore
Programme Details:
4:30pm 5:00pm:Registration
5:00pm 6:15pm:Keynote Presentation and Q&A
6:15pm 6:30pm:Networking
Speaker's Profile:
Stefan Conrady, Managing Partner, Bayesia USA & Singapore
Stefan Conrady has over 20 years of experience in analytics, marketing, and strategic planning with leading automotive brands, such as Mercedes-Benz, BMW, and Rolls-Royce Motor Cars. Stefan is a native of Ulm, Germany, but his career has spanned the globe, having lived and worked in Chicago, New York, Munich, and Singapore, just to name a few. In his most recent corporate assignment, he was heading the Analytics & Forecasting Group at Nissan North America.
Today, in his role as Managing Partner of Bayesia USA and Bayesia Singapore, he is recognised as a thought leader in applying AI for research, analytics, and reasoning. Stefan's tutorials, seminars, and lectures on Bayesian Networks are widely followed by scientists who embrace AI innovations to accelerate applied research. In this context, Stefan has recently co-authored a book with Lionel Jouffe, Bayesian Networks & BayesiaLab A Practical Introduction for Researchers.
