Description
An introduction to methods for automated learning of relationships on the basis of empirical data. Includes topics in supervised and unsupervised machine learning and deeper knowledge of specific algorithms and their applications. Evaluation and quantification of performance of ML systems. Discussion of data ethics.
Prerequisites
- Prerequisite(s): COMP 2402 and ( COMP 2404 or SYSC 3010 or SYSC 3110 ) and COMP 2804 and ( MATH 1104 or MATH 1107 ).
Conditions and arrangements
- Precludes additional credit for Precludes additional credit for COMP 4105 (no longer offered), SYSC 4415 .
- Prerequisite(s): COMP 2402 and ( COMP 2404 or SYSC 3010 or SYSC 3110 ) and COMP 2804 and ( MATH 1104 or MATH 1107 ).
- Lectures three hours a week.
Reference text in its original language
An introduction to methods for automated learning of relationships on the basis of empirical data. Includes topics in supervised and unsupervised machine learning and deeper knowledge of specific algorithms and their applications. Evaluation and quantification of performance of ML systems. Discussion of data ethics.
- Prerequisite(s): COMP 2402 and ( COMP 2404 or SYSC 3010 or SYSC 3110 ) and COMP 2804 and ( MATH 1104 or MATH 1107 ).
- Precludes additional credit for Precludes additional credit for COMP 4105 (no longer offered), SYSC 4415 .
- Lectures three hours a week.
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://calendar.carleton.ca/undergrad/courses/COMP/