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X-WR-CALNAME:Mathematical Sciences
X-ORIGINAL-URL:https://uwm.edu/math
X-WR-CALDESC:Events for Mathematical Sciences
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DTSTART;TZID=America/Chicago:20250221T120000
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DTSTAMP:20260614T161916
CREATED:20250219T140056Z
LAST-MODIFIED:20250219T140056Z
UID:10016209-1740139200-1740144600@uwm.edu
SUMMARY:Community of Practice:  Supporting Students in Math (SupportU)
DESCRIPTION:Community of Practice: Supporting Students in Math (SupportU)\nHave you interacted with a student that you were especially concerned about\, but you didn’t quite know what to say\, who to tell\, or what to do? Perhaps the student confided in you about a serious personal issue\, said something that led you to worry about them\, or acted in a way that concerned you. We welcome Dr. Becky Freer\, the Associate Dean of Students\, who will facilitate a training on supporting students. You’ll learn tools to identify and support students who may be experiencing challenges or crises. You’ll also learn about how to make referrals\, seek assistance\, and connect students to the Dean of Students Office Case Managers as well as campus and community resources.\n\nFacilitated by Dr. Becky Freer\, Associate Dean of Students
URL:https://uwm.edu/math/event/community-of-practice-supporting-students-in-math-supportu/
LOCATION:EMS Building\, E495\, 3200 N Cramer St\, Milwaukee\, WI\, United States
CATEGORIES:Seminars
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CREATED:20250113T152601Z
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UID:10016197-1740146400-1740150000@uwm.edu
SUMMARY:Colloquium: Mr. Mike Clutterbuck
DESCRIPTION:Embeddings: The Language of AI\nMr. Mike Clutterbuck\nLead Data Scientist\nWantable Inc. \nEmbeddings are a core concept in machine learning that help AI understand and organize complex data. They take things like words\, images\, or user behavior and turn them into compact numerical representations\, making it easier for AI to spot patterns and relationships. This is how LLMs understand language and recommendation systems personalize content. \nMathematically\, embeddings work by placing similar items closer together in a structured space\, using techniques like matrix factorization\, neural networks\, and dimensionality reduction. This makes them essential for search\, personalization\, fraud detection\, next-word prediction\, and more. While they might seem abstract\, embeddings are working behind the scenes in many of the AI-powered products and tools we use today
URL:https://uwm.edu/math/event/colloquium-mike-clutterbuck/
LOCATION:EMS Building\, E495\, 3200 N Cramer St\, Milwaukee\, WI\, United States
CATEGORIES:Colloquia
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