此參考說明所註明的目錄。請向該機構確認當前課程供給與適用於你入學的條件。
課程說明
實證方法：經濟與商業中的機器學習 從統計、理論與計算的角度介紹「現代」統計學習與預測建模；並含應用範例。主題可能包括線性與局部（基於鄰近）回歸方法、邏輯回歸與判別分析分類方法、交叉驗證與自助重抽樣方法、模型選擇與正則化、分類樹與支持向量機。講課、實驗室，每週 4 小時。限制：開放予 ECON（單科或聯合）、ECAN、BBE、INPE 主修以及 ECON 輔修學生，直到登記指南中指定的日期。先修課程：ECON 2P30 或 3P91；ECON 3P90。註：此課程可能以多種教學方式提供。教學方式將在適用學期的學術時刻表上列出。完成此課程將取代先前取得之成績與學分，該先前成績與學分為
先修條件
- 先修課程：ECON 2P30 或 3P91；ECON 3P90。
條件與方式
- 限制：開放給 ECON（單主修或聯合主修）、ECAN、BBE、INPE 主修及 ECON 副修，至註冊指南所示之日期為止。
- 先修課程：ECON 2P30 或 3P91；ECON 3P90。
- 註：此課程可能以多種教學方式提供。教學方式將在適用學期的學術時刻表上列出。完成此課程將取代先前取得之成績與學分，該先前成績與學分為
原文參考文本
Empirical Methods: Machine Learning in Economics and Business Introduction to "modern" statistical learning and predictive modeling from statistical, theoretical, and computational perspectives; with applications. Topics may include linear and local (neighbour-based) regression methods, logistic regression and discriminant analysis methods of classification, cross validation and bootstrap resampling methods, model selection and regularization, classification trees, and support vector machines. Lectures, lab, 4 hours per week. Restriction: open to ECON (single or combined), ECAN, BBE, INPE majors and ECON minors until date specified in Registration guide. Prerequisite(s): ECON 2P30 or 3P91 ; ECON 3P90 . Note: this course may be offered in multiple modes of delivery. The method of delivery will be listed on the academic timetable, in the applicable term.Completion of this course will replace previous assigned grade and credit obtained in
- Prerequisite(s): ECON 2P30 or 3P91 ; ECON 3P90 .
- Restriction: open to ECON (single or combined), ECAN, BBE, INPE majors and ECON minors until date specified in Registration guide.
- Note: this course may be offered in multiple modes of delivery. The method of delivery will be listed on the academic timetable, in the applicable term.Completion of this course will replace previous assigned grade and credit obtained in
來源與參考
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來源參考 : https://brocku.ca/webcal/2024/undergrad/econ.html