Brock University · ECON 4P07

Introduction to Empirical Methods: Machine Learning in Economics and Business

Official title : Empirical Methods: Machine Learning in Economics and Business Introduction to

Credits : 0.5Reference year : 2024-25

This reference describes the indicated catalogue. Check with the institution to confirm the current offer and the conditions applicable to your intake.

Description

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

Prerequisites

  • Prerequisite(s): ECON 2P30 or 3P91 ; ECON 3P90 .

Conditions and arrangements

  • 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
Reference text in its original language

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

Sources and references

Dates and sources are retained to help you verify the information. Translations are provided to facilitate reading; the official source governs conditions and requirements.

Source reference : https://brocku.ca/webcal/2024/undergrad/econ.html

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