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描述
实证方法：经济与商业中的机器学习 从统计、理论和计算视角介绍“现代”统计学习与预测建模；并含应用。主题可能包括线性与局部（基于邻域）回归方法、逻辑回归与判别分析的分类方法、交叉验证与自助抽样重采样方法、模型选择与正则化、分类树和支持向量机。讲座、实验，每周4小时。限制：在登记指南指定日期之前，仅向 ECON（单专业或联合专业）、ECAN、BBE、INPE 专业和 ECON 辅修学生开放。先修课：ECON 2P30 或 3P91；ECON 3P90。注：本课程可能以多种授课方式提供。授课方式将列在学术课程表的相应学期中。完成本课程将取代先前获得的成绩与学分
先修课程
- 先修课：ECON 2P30 或 3P91；ECON 3P90。
条件与方式
- 限制：在登记指南指定日期之前，向 ECON（单学科或联合学科）、ECAN、BBE、INPE 专业和 ECON 副修开放。
- 先修课：ECON 2P30 或 3P91；ECON 3P90。
- 注：本课程可能以多种授课方式提供。授课方式将列在学术课程表的相应学期中。完成本课程将取代先前获得的成绩与学分
原文参考文本
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
来源与参考
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来源参考 : https://brocku.ca/webcal/2024/undergrad/econ.html