BEGIN:VCALENDAR
VERSION:2.0
METHOD:PUBLISH
CALSCALE:GREGORIAN
PRODID:-//WordPress - MECv7.34.1//EN
X-ORIGINAL-URL:https://datasciences.utoronto.ca/
X-WR-CALNAME:DSI
X-WR-CALDESC:A hub for data science activity at the University of Toronto.
X-WR-TIMEZONE:America/Toronto
BEGIN:VTIMEZONE
TZID:America/Toronto
X-LIC-LOCATION:America/Toronto
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20260308T030000
RRULE:FREQ=YEARLY;BYMONTH=03;BYDAY=2SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20261101T010000
RRULE:FREQ=YEARLY;BYMONTH=11;BYDAY=1SU
END:STANDARD
END:VTIMEZONE
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-PUBLISHED-TTL:PT1H
X-MS-OLK-FORCEINSPECTOROPEN:TRUE
BEGIN:VEVENT
CLASS:PUBLIC
UID:MEC-647bba344396e7c8170902bcf2e15551@datasciences.utoronto.ca
DTSTART;TZID=America/Toronto:20210315T171500
DTEND;TZID=America/Toronto:20210315T181500
DTSTAMP:20210616T193724Z
CREATED:20210616
LAST-MODIFIED:20220805
PRIORITY:5
SEQUENCE:0
TRANSP:OPAQUE
SUMMARY:DSSS@UofT: Jesse Cresswell
DESCRIPTION:Join us for the next installment of the Data Science Speaker Series at U of T with:\nJesse Cresswell, PhD\nMachine Learning Scientist, Layer 6 AI at TD\nFree Event | Registration Required\nTalk Title:\nEvaluating Model Performance on Highly Imbalanced Datasets\nAbstract:\nImbalanced datasets are extremely common in real-world data science problems, and techniques are widely known for handling imbalance when modelling. After modelling, meaningful evaluation and comparison of models remains a challenging task in its own right. In this seminar I will discuss the ways that dataset imbalance can confound our use of typical model performance metrics, and present challenges for fair comparisons. I will introduce advanced metrics which retain desirable properties even as datasets become skewed, and which give theoretical assurance that cross-model or cross-dataset comparisons are fair. Using these metrics can ensure better model selection in the face of imbalance.gaming.\nSpeaker Profile:\nJesse is a Machine Learning Scientist at Layer 6 AI within TD, and is the Team Lead for US Credit Risk, TD Insurance, and Quantum Machine Learning. His applied work centers on building machine learning models in high risk and highly regulated domains. Jesse maintains a keen interest in quantum computing and its implications for machine leaning, as well as reinforcement learning.\nBefore joining Layer 6, Jesse completed his PhD at the Department of Physics, University of Toronto. His research at the intersection of quantum computing and string theory was supported by a Vanier Scholarship.\n
URL:https://datasciences.utoronto.ca/event/data-sciences-speaker-series-at-uoft-jesse-cresswell/
CATEGORIES:DSSS@ U of T - 2021-2022 Season,DSSS@U of T
ATTACH;FMTTYPE=image/png:https://datasciences.utoronto.ca/wp-content/uploads/2021/06/Cresswell-Jesse-v1.png
END:VEVENT
END:VCALENDAR