서울대학교

선생님, 안녕하십니까?

2024년 겨울, 서울대학교 과학철학 분야 외국학자 초청강연을 개최합니다.
과학데이터혁신연구소와 철학사상연구소 주최로 열리는 이번 강연의 주제는 “인공지능의 시대 통계학에 관해 생각하기(Thinking About Statistics in the Era of AI)”이며, 강연자는 교토대학의 준 오츠카(Jun Otsuka) 교수입니다.
12월 17일과 18일 이틀동안 열리는 이번 강연에서, 오츠카 교수와 함께 통계학의 철학적 토대에 관해 깊이 생각해보고자 합니다. 많은 참여와 관심 바랍니다.


참석을 위해서는 아래 링크를 통해 등록해주시기 바랍니다.

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강연주제: Thinking About Statistics in the Era of AI


연사: Jun Otsuka 교수 (Kyoto University)

사회: 천현득 교수 (서울대학교)


프로그램 (Program)



Lecture 1. Thinking About Statistics I: Bridging the gap between statistics and epistemology



일시: 2024년 12월 17일(화) 오후 4시-6시

장소: 서울대학교 25동 208호

요약: In this lecture series, we will explore the philosophical foundations of statistics. The first session focuses on the epistemological characteristics of Bayesian and frequentist statistics. Inferential statistics makes inductive inferences by modeling the “uniformity of nature” underlying the data through statistical models and estimating their parameters. Bayesian and frequentist approaches represent distinct epistemologies, each espousing a different conception as to what counts as “justified inference”. Specifically, Bayesian statistics is characterized by an internalist epistemology, justifying the posterior distribution as a derivation from prior beliefs, whereas frequentist statistics is aligned with an externalist epistemology, justifying conclusions (e.g., rejection of the null hypothesis) through reliable methods. By bridging philosophy and statistics in this way, we not only clarify the nature of each statistical approach but also gain insights into the issues they face, such as the problem of prior probabilities in Bayesian statistics and the p-value problem in frequentist statistics.



Lecture 2. Thinking About Statistics II: Ontological implications of model selection, machine learning, and causal inference



일시: 2024년 12월 18일(수) 오후 4시-6시

장소: 서울대학교 25동 208호

요약: In the second session, we will discuss the philosophical implications of machine learning methods developed since the late 20th century, including model selection, deep learning, and causal inference, with a particular focus on their ontological characteristics. From the ontological perspective—namely how the underlying "uniformity of nature" is modeled—model selection theory, exemplified by AIC (Akaike Information Criterion), and deep learning models can be understood as methods for identifying "real patterns" (à la Dennett) that support effective predictions and extrapolations based on data. In contrast, causal inference, which aims to go beyond mere prediction and evaluate intervention outcomes, introduces an additional ontological layer that models possible worlds and the laws governing transitions between these worlds. These ontological considerations will clarify the strengths and challenges of each inductive methodology.




강연자 소개: Jun Otsuka is a philosopher of science, focusing particularly on the philosophy of statistics, machine learning, and evolutionary biology. He holds the position of Associate Professor of Philosophy at Kyoto University and serves as a visiting researcher at the RIKEN Center for Advanced Intelligence Project in Japan. His works have been published in various scientific and philosophical journals, including Philosophy of Science, the British Journal for the Philosophy of Science, Philosophical Transactions of the Royal Society, and Proceedings of Machine Learning Research.



주최: 과학데이터혁신연구소, 철학사상연구소

후원: 서울대학교 G-LAMP 사업단

문의: 구본진 연구원 (koobon1998@snu.ac.kr)