BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//FutureCite - ECPv6.17.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:FutureCite
X-ORIGINAL-URL:https://futurecite.com
X-WR-CALDESC:Events for FutureCite
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/Chicago
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20200308T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20201101T070000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20210314T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20211107T070000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0600
TZOFFSETTO:-0500
TZNAME:CDT
DTSTART:20220313T080000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0500
TZOFFSETTO:-0600
TZNAME:CST
DTSTART:20221106T070000
END:STANDARD
END:VTIMEZONE
BEGIN:VTIMEZONE
TZID:America/Edmonton
BEGIN:DAYLIGHT
TZOFFSETFROM:-0700
TZOFFSETTO:-0600
TZNAME:MDT
DTSTART:20200308T090000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0600
TZOFFSETTO:-0700
TZNAME:MST
DTSTART:20201101T080000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0700
TZOFFSETTO:-0600
TZNAME:MDT
DTSTART:20210314T090000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0600
TZOFFSETTO:-0700
TZNAME:MST
DTSTART:20211107T080000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0700
TZOFFSETTO:-0600
TZNAME:MDT
DTSTART:20220313T090000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0600
TZOFFSETTO:-0700
TZNAME:MST
DTSTART:20221106T080000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/Chicago:20210120T080000
DTEND;TZID=America/Chicago:20210310T170000
DTSTAMP:20201114T005902Z
CREATED:20201114T005902Z
LAST-MODIFIED:20201114T005902Z
UID:14532-1611129600-1615395600@futurecite.com
SUMMARY:Courses: AI Strategy and Management - Winter 2021
DESCRIPTION:AI Strategy and Management – Winter 2021\n\n\nWhen\nJan. 20\, 2021 – March 10\, 2021\nVarious\n\n\nWhere\n\nOnline \n\n\n\n\n\n \n\n\n\n\n\n\nSecure your future with the knowledge\, tools and confidence to launch AI projects and manage technical teams.\n\n\n\n\n\n\nDetails:\nModern businesses recognize the transformative power of artificial intelligence but are also facing a severe shortage of skilled workers. The demand for AI and machine learning (ML) expertise is growing across all industries\, and it’s through the marriage of domain expertise with technical ML skills that companies can gain competitive advantages. \nOur eight-week online course combines synchronous (collaborative) and asynchronous (independent) lectures\, readings and discussions\, as well as practical assignments and a final capstone project. In the final project\, you will work in a group to complete a Machine Learning Project Charter for a specific ML project\, and pitch your project to a panel of ML and business experts. \nYou’ll graduate from the course with the: \n\nSkills to draft AI adoption strategy for your organization\nAbility to identify ML projects\, assemble teams\, and execute related business tactics\nConfidence to manage and interact with technical teams\n\nWhat is the goal of this course?\nThe purpose of this course is to develop your knowledge and skills to define and manage a machine learning project that supports your organization’s business priorities. This isn’t a technical course\, but we will get into enough technical details to empower you to have informed discussions with technical teams about the data needed for ML projects\, appropriate approaches to the problem\, and the evaluation of the results. You’ll also have an appreciation of the business\, social and ethical impacts of ML and how they apply to your organization and project. \nLearning Objectives\n\nUnderstand the considerations required for selecting an appropriate machine learning project in the context of the organization’s capabilities and objectives\nImplement some organizational processes to apply ethical thinking to machine learning projects\nIntelligently discuss data issues with data professionals\nUnderstand and discuss the uses\, strengths and limitations of the three types of machine learning\nEffectively apply the ML Project Charter to think through and pitch an ML project\n\n\n\n\n\n\n\n\n\n\nWe look forward to having you join us in January!\n\n\nAre you curious if AI Strategy Management is the right fit for you? Our program Guide outlines all of the details including prerequisites\, module overviews and FAQs. \nStephanie Husby\, pictured above\, will be one of your instructors. Passionate about translating difficult topics into easy-to-access concepts and terminology\, she strives to bridge the gap between business and technical experts. \n\n\n\n\nDownload the Program Guide https://app.hubspot.com/documents/4771956/view/96053384?accessId=b978df\n\n\n\n\n\n\n\n\nReady to Apply?\n\n\nNow more than ever\, businesses recognize the transformative power of artificial intelligence (AI) but are also facing a severe shortage of skilled workers. \nCourse Fee is grant eligible.  See Program Guide. \nThe demand for AI and machine learning (ML) expertise is growing across all industries\, and it’s through the marriage of domain expertise with ML understanding that companies can gain competitive advantages. \n\n\n\nApply Today
URL:https://futurecite.com/event/courses-ai-strategy-and-management-winter-2021/
LOCATION:Virtual Online
CATEGORIES:AI / ML / Data Science,Courses,Digital Transformation,Education,Entrepreneurs,Innovation,Learning,Science,Technology,Webinar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Edmonton:20210326T120000
DTEND;TZID=America/Edmonton:20210326T130000
DTSTAMP:20210316T235525Z
CREATED:20210316T235525Z
LAST-MODIFIED:20210316T235525Z
UID:15465-1616760000-1616763600@futurecite.com
SUMMARY:AMII AI Seminars: Two-player\, Zero-sum games
DESCRIPTION:Amii researcher at the University of Alberta Dustin Morrill presents “Hindsight Rationality and Efficient Deviation Types in Extensive-Form Games”. In this talk\, he suggests to field learning algorithms that ensure strong performance in hindsight relative to “deviations” (pre-defined behaviour modifications) in two-player\, zero-sum games. \nAbstract: A successful approach to playing two-player\, zero-sum games has been to deploy a static artifact resembling a Nash equilibrium\, which has led work in artificial intelligence to focus on computing such artifacts. This approach is less sound and has been less successful in multi-player\, general-sum games. We suggest instead to field learning algorithms that ensure strong performance in hindsight relative to “deviations”\, i.e.\, pre-defined behavior modifications. A society of such “hindsight rational” agents converges toward mediated equilibrium\, a traditional notion of equilibrium based on average correlated play rather than factored behavior\, in contrast to Nash equilibrium. We re-examine deviation types and mediated equilibria in extensive-form games to gain a more complete understanding and resolve past misconceptions. We introduce a new deviation type that has implicitly formed the basis for the counterfactual regret minimization (CFR) algorithm. We generalize CFR to the extensive-form regret minimization (EFR) algorithm that is hindsight rational for any given deviation type within a broad and natural class. This class contains powerful new deviation types that are efficient to use in games with moderate lengths. We present an empirical analysis of EFR’s performance with different deviation types in common benchmark games\, showing that stronger deviation types typically impart better performance\, even in two-player\, zero-sum games. \nPresenter Bio: Dustin is a Ph.D. candidate at the University of Alberta and the Alberta Machine Intelligence Institute (Amii) working with Professor Michael Bowling. He works on multi-agent learning and scaleable\, dependable learning algorithms. He is a coauthor of [DeepStack] and he created [Cepheus’s public match interface]. He completed a B.Sc. and M.Sc. in computing science at the University of Alberta where his M.Sc was also supervised by Michael Bowling. As an undergraduate\, he worked with the Computer Poker Research Group (CPRG) to create an [open-source web interface to play against poker bots] and to develop the 1st-place 3-player Kuhn poker entry in the 2014 Annual Computer Poker Competition (ACPC).
URL:https://futurecite.com/event/amii-ai-seminars-two-player-zero-sum-games/
LOCATION:Virtual Online
CATEGORIES:AI / ML / Data Science,Education,Entrepreneurs,Human Talent,Innovation,Learning,Research,Students,Technology,Webinar
END:VEVENT
END:VCALENDAR