Jannes Klaas's guest appearances

Jannes Klaas

Datacast

David Sweet

Sara Hooker

Brian O'Neill

Cody Coleman

Sarah Catanzaro

Jonathan Leslie

Vincent Tatan

Ben Fowler

Admond Lee

Ankit Gupta

Conor Dewey

Christopher Peters

Leni Krsova

Francisco Javier Carrera Arias

Matthias Raess

OwlTail

Show Notes:(1:57) Jannes discussed his undergraduate experience studying International Business Administration at Rotterdam School of Management - Erasmus University (one of Europe’s top 10 research-based business schools).(3:06) Jannes talked about his work on the IHS Global Green City Index, a set of measurements that can give majors actionable insight in where cities stand in sustainability.(4:05) Jannes talked about his involvement with the Turing Society in Rotterdam, where he developed a new kind of machine learning class called “Bletchley Bootcamp for Machine Learning in Financial Context".(5:20) Jannes discussed how he built out the materials and inviting guest lectures for his machine learning for finance class.(6:57) Jannes talked about his decision to pursue a Master’s degree in Financial Economics at Said Business School, a part of Oxford University.(8:04) Jannes went over the most useful graduate course that he took for the Master’s degree, called “Information and Communication in Finance."(11:23) Jannes discussed his role with the Oxford Artificial Intelligence Society, which provides a platform to educate, build, connect, and employ an AI community that constantly drives innovation for the university and the world.(14:23) Jannes shared his thoughts regarding challenges of bringing different perspectives into the conversations around AI.(15:40) Jannes shared a brief overview of his current employer, QuantumBlack, and an example project with Formula 1 to optimize pitch stops.(18:12) Moving on to discuss his book “Machine Learning For Finance” (which introduces the study of machine learning and deep learning algorithms for financial practitioners), Jannes went over his motivation as well as the ideal audience.(19:27) Jannes went over in details the process of writing this book.(22:39) Jannes recommended a couple of resources for people who are new to Time Series forecasting.(23:57) Jannes revealed how he uses Twitter to keep up-to-date with NLP research.(25:54) Jannes explored 2 powerful financial applications of generative models: (1) perform synthetic data generation that can help with data labeling efforts and (2) generate realistic time series.(27:59) Jannes explained why private equity is an exciting playground for Reinforcement Learning models.(29:05) Jannes shared his opinions on common approaches to do Reinforcement Learning in the finance industry.(32:13) Jannes recommended the best practices to deploy machine learning models into production.(34:26) Jannes shared some interesting research projects of ethics and fairness in machine learning that attracts his attention.(36:56) Jannes shared how his financial economics background contributes to him being a good data scientist.(38:11) Jannes shared his thoughts on the tech and data community in London.(38:56) Closing segment.His Contact Info:LinkedInTwitterGitHubMediumHis Recommended Resources:Machine Learning For FinanceOxford Internet InstituteQuant GANs: Deep Generation of Financial Time Series paperMLFlow Open-Source Platform for End-to-End ML LifecycleProfessor Sandra Wachter’s paper: “Affinity Profiling and Discrimination by Association in Online Behavioral Advertising"Two SigmaBenevolentAIComputer Age Statistical Inference by Bradley Efron and Trevor Hastie

Show Notes:<ul><li>(1:57) Jannes discussed his undergraduate experience studying International Business Administration at <a href="https://www.rsm.nl/" rel="nofollow" target="_blank">Rotterdam School of Management - Erasmus University</a> (one of Europe’s top 10 research-based business schools).</li><li>(3:06) Jannes talked about his work on the <a href="https://www.mdpi.com/2071-1050/10/6/2031" rel="nofollow" target="_blank">IHS Global Green City Index</a>, a set of measurements that can give majors actionable insight in where cities stand in sustainability.</li><li>(4:05) Jannes talked about his involvement with the <a href="https://tsociety.io/" rel="nofollow" target="_blank">Turing Society</a> in Rotterdam, where he developed a new kind of machine learning class called “<a href="https://www.slideshare.net/slideshow/embed_code/key/iJNsPYXD3JWP8P" rel="nofollow" target="_blank">Bletchley Bootcamp for Machine Learning in Financial Context</a>".</li><li>(5:20) Jannes discussed how he built out the materials and inviting guest lectures for his machine learning for finance class.</li><li>(6:57) Jannes talked about his decision to pursue a Master’s degree in Financial Economics at <a href="https://www.sbs.ox.ac.uk/" rel="nofollow" target="_blank">Said Business School</a>, a part of Oxford University.</li><li>(8:04) Jannes went over the most useful graduate course that he took for the Master’s degree, called “Information and Communication in Finance."</li><li>(11:23) Jannes discussed his role with <a href="https://oxai.org/" rel="nofollow" target="_blank">the Oxford Artificial Intelligence Society</a>, which provides a platform to educate, build, connect, and employ an AI community that constantly drives innovation for the university and the world.</li><li>(14:23) Jannes shared his thoughts regarding challenges of bringing different perspectives into the conversations around AI.</li><li>(15:40) Jannes shared a brief overview of his current employer, <a href="https://www.quantumblack.com/" rel="nofollow" target="_blank">QuantumBlack</a>, and an example project with Formula 1 to optimize pitch stops.</li><li>(18:12) Moving on to discuss his book “<a href="https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-finance" rel="nofollow" target="_blank">Machine Learning For Finance</a>” (which introduces the study of machine learning and deep learning algorithms for financial practitioners), Jannes went over his motivation as well as the ideal audience.</li><li>(19:27) Jannes went over in details the process of writing this book.</li><li>(22:39) Jannes recommended a couple of resources for people who are new to Time Series forecasting.</li><li>(23:57) Jannes revealed how he uses Twitter to keep up-to-date with NLP research.</li><li>(25:54) Jannes explored 2 powerful financial applications of generative models: (1) perform synthetic data generation that can help with data labeling efforts and (2) generate realistic time series.</li><li>(27:59) Jannes explained why private equity is an exciting playground for Reinforcement Learning models.</li><li>(29:05) Jannes shared his opinions on common approaches to do Reinforcement Learning in the finance industry.</li><li>(32:13) Jannes recommended the best practices to deploy machine learning models into production.</li><li>(34:26) Jannes shared some interesting research projects of ethics and fairness in machine learning that attracts his attention.</li><li>(36:56) Jannes shared how his financial economics background contributes to him being a good data scientist.</li><li>(38:11) Jannes shared his thoughts on the tech and data community in London.</li><li>(38:56) Closing segment.</li></ul>His Contact Info:<ul><li><a href="https://www.linkedin.com/in/jannes-klaas/" rel="nofollow" target="_blank">LinkedIn</a></li><li><a href="https://twitter.com/jannesklaas" rel="nofollow" target="_blank">Twitter</a></li><li><a href="https://github.com/JannesKlaas" rel="nofollow" target="_blank">GitHub</a></li><li><a href="https://medium.com/@jannesklaas" rel="nofollow" target="_blank">Medium</a></li></ul>His Recommended Resources:<ul><li><a href="https://www.oreilly.com/library/view/machine-learning-for/9781789136364/" rel="nofollow" target="_blank">Machine Learning For Finance</a></li><li><a href="https://www.oii.ox.ac.uk/" rel="nofollow" target="_blank">Oxford Internet Institute</a></li><li><a href="https://arxiv.org/abs/1907.06673" rel="nofollow" target="_blank">Quant GANs: Deep Generation of Financial Time Series</a> paper</li><li><a href="https://mlflow.org/" rel="nofollow" target="_blank">MLFlow Open-Source Platform for End-to-End ML Lifecycle</a></li><li><a href="https://twitter.com/SandraWachter5" rel="nofollow" target="_blank">Professor Sandra Wachter</a>’s paper: “<a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3388639" rel="nofollow" target="_blank">Affinity Profiling and Discrimination by Association in Online Behavioral Advertising</a>"</li><li><a href="https://www.twosigma.com/" rel="nofollow" target="_blank">Two Sigma</a></li><li><a href="https://benevolent.ai/" rel="nofollow" target="_blank">BenevolentAI</a></li><li><a href="https://www.amazon.com/Computer-Age-Statistical-Inference-Mathematical/dp/1107149894" rel="nofollow" target="_blank">Computer Age Statistical Inference</a> by Bradley Efron and Trevor Hastie</li></ul>

Episode 23: Machine Learning for Finance with Jannes Klaas