BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Diogo Gomes (KAUST) DTSTART:20230504T160000Z DTEND:20230504T170000Z DTSTAMP:20260423T003255Z UID:MPML/103 DESCRIPTION:Title: M athematics for data science and AI - curriculum design\, experiences\, and lessons learned\nby Diogo Gomes (KAUST) as part of Mathematics\, Phys ics and Machine Learning (IST\, Lisbon)\n\n\nAbstract\nIn this talk\, we w ill explore the importance of mathematical foundations for AI and data sci ence and the design of an academic curriculum for graduate students. While traditional mathematics for AI and data science has focused on core techn iques like linear algebra\, basic probability\, and optimization methods ( e.g.\, gradient and stochastic gradient descent)\, several advanced mathem atical techniques are now essential to understanding modern data science. These include ideas from the calculus of variations in spaces of random va riables\, functional analytic methods\, ergodic theory\, control theory me thods in reinforcement learning\, and metrics in spaces of probability mea sures. We will discuss the author's experience designing an applied mathem atics curriculum on data science and draw on the author's experience and l essons learned in teaching an advanced course on the mathematical foundati ons of data science. This talk aims to promote discussion and exchange of ideas on how mathematicians can play an important role in AI and data scie nce and better equip our students to excel in this field.\n LOCATION:https://researchseminars.org/talk/MPML/103/ END:VEVENT END:VCALENDAR