Time:
10/29/2022 06:00 PM PDT
10/29/2022 07:00 PM MDT
10/29/2022 08:00 PM CDT
10/29/2022 09:00 PM EDT
10/30/2022 01:00 AM GMT
10/30/2022 02:00 AM CET
10/30/2022 09:00 AM Taiwan
10/29/2022 06:00 PM PDT
10/29/2022 07:00 PM MDT
10/29/2022 08:00 PM CDT
10/29/2022 09:00 PM EDT
10/30/2022 01:00 AM GMT
10/30/2022 02:00 AM CET
10/30/2022 09:00 AM Taiwan
研究領域 (Field):
Computer Science
研究子領域 (Sub-field):
TrustworthyAI, Recommender Systems
其他關鍵字 (Supplementary keywords):
interpretable machine learning; explainable artificial intelligence
Computer Science
研究子領域 (Sub-field):
TrustworthyAI, Recommender Systems
其他關鍵字 (Supplementary keywords):
interpretable machine learning; explainable artificial intelligence
Abstract:
Despite the remarkable achievement of machine learning in various fields, such as music recommendations of Spotify and neural language translation of Google, designing responsible and accountable ML systems remains a significant problem because of the practical requirements of the stakeholders and the regulations in different domains. Interpretable machine learning hereby provides the transparency of the particular decisions made by machine learning systems. The interpretable machine learning tackles the critical problem that humans cannot understand the behaviors of complex machine learning models and how these models arrive at a particular decision. From this perspective, this talk will mainly focus on introducing interpretable machine learning from both theoretical aspects and its applications.
Despite the remarkable achievement of machine learning in various fields, such as music recommendations of Spotify and neural language translation of Google, designing responsible and accountable ML systems remains a significant problem because of the practical requirements of the stakeholders and the regulations in different domains. Interpretable machine learning hereby provides the transparency of the particular decisions made by machine learning systems. The interpretable machine learning tackles the critical problem that humans cannot understand the behaviors of complex machine learning models and how these models arrive at a particular decision. From this perspective, this talk will mainly focus on introducing interpretable machine learning from both theoretical aspects and its applications.
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