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本頁翻譯進度

燈號說明

審定:無
翻譯:林菡(簡介並寄信)
編輯:侯嘉玨(簡介並寄信)



概要

機率理論涵蓋了人類認知中的許多重要特徵,包括感知、推理、信念改變和學習方面。在人們開始編製機率法則之前,信念程度的表達很早就已經在語言中使用了。本課程探索關於機率法則形成的歷史和爭論,機率理論如何運用於具體的認知過程,它與人類對因果的理解有著怎樣的關係,以及新的關於因果模型計算方法怎樣為理解人類的機率推理過程提供了一個架構。

必備先修

本課程適合認知科學、人工智慧和相關領域專業的高年級本科生或研究生。修讀過一門認知科學的課程,以及一門機率論或統計學的課程會更有幫助。

課堂政策

每周兩個小時。希望學生完成每周閱讀以備課堂討論。

在本學期的課程中,學生必須根據課程閱讀的內容做一個帶註解的參考書目︰對於每份閱讀資料,學生必須就閱讀資料的重點及其如何與課堂主題結合寫一份簡短的概要(大約一段)。

學生需要設計、執行和報告一個原創研究實驗,用來探索如何用機率理論和(或)因果架構將人類行為模式化。





Overview

Probability theory captures a number of essential characteristics of human cognition, including aspects of perception, reasoning, belief revision, and learning. Expressions of degree of belief were used in language long before people began codifying the laws of probability theory. This course explores the history and debates over codifying the laws of probability, how probability theory applies to specific cognitive processes, how it relates to the human understanding of causality, and how new computational approaches to causal modeling provide a framework for understanding human probabilistic reasoning.

Prerequisites

This class is suitable for advanced undergraduates or graduate students specializing in cognitive science, artificial intelligence, and related fields. A course in cognitive science, and a course in probability or statistics, are helpful.

Class Policies

Class meets for two hours per week. Students are expected to do weekly readings in preparation for discussions during class.

Over the course of the semester, students will be required to produce an annotated bibliography based on the course readings: for each reading, students must write a short (approximately one paragraph) summary of the important points of the reading, and how that reading fits into the themes of the class.

Students will also be required to design, execute, and report on an original research experiment exploring how human behavior can be modeled using probability theory and/or causal structures.




 
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