January 23, 2019
12:00PM - 1:00PM
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2019-01-23 13:00:00
2019-01-23 14:00:00
Online Colloquium: Michael I. Jordan - Machine Learning: Dynamical, Economic and Stochastic Perspectives
Michael I. Jordan
Pehong Chen Distinguished Professor, Department of Statistics, University of California, Berkeley
While there has been significant progress in the theory and practice in machine learning in recent years, many fundamental challenges remain. Some are mathematical in nature, such as the challenges associated with optimization and sampling in high-dimensional spaces. Some are statistical in nature, including the challenges associated with multiple decision-making. Others are economic in nature, including the need to price services and provide incentives in learning-based two-way markets. I will present recent progress on each of these fronts.
Click here for detailed instructions on how to participate.
Participate virtually
OSU ASC Drupal 8
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America/New_York
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Date Range
Add to Calendar
2019-01-23 12:00:00
2019-01-23 13:00:00
Online Colloquium: Michael I. Jordan - Machine Learning: Dynamical, Economic and Stochastic Perspectives
Michael I. Jordan
Pehong Chen Distinguished Professor, Department of Statistics, University of California, Berkeley
While there has been significant progress in the theory and practice in machine learning in recent years, many fundamental challenges remain. Some are mathematical in nature, such as the challenges associated with optimization and sampling in high-dimensional spaces. Some are statistical in nature, including the challenges associated with multiple decision-making. Others are economic in nature, including the need to price services and provide incentives in learning-based two-way markets. I will present recent progress on each of these fronts.
Click here for detailed instructions on how to participate.
Participate virtually
Mathematical Biosciences Institute
mbi-webmaster@osu.edu
America/New_York
public
Michael I. Jordan
Pehong Chen Distinguished Professor, Department of Statistics, University of California, Berkeley
While there has been significant progress in the theory and practice in machine learning in recent years, many fundamental challenges remain. Some are mathematical in nature, such as the challenges associated with optimization and sampling in high-dimensional spaces. Some are statistical in nature, including the challenges associated with multiple decision-making. Others are economic in nature, including the need to price services and provide incentives in learning-based two-way markets. I will present recent progress on each of these fronts.
