Kadenze

Learning Outcomes

Audio Classification

• Have a basic applied understanding of various supervised learning algorithms such as naive bayes, support vector machines, neural networks and decision trees.

• Understanding of how they can be applied to MIR tasks such as genre classification and instrument classification.

Tagging

• Understanding of the different ways tags for describing music tracks can be obtained (surveys, games with a purpose, auto-tagging).

• Ability to formulate and understand auto-tagging as a machine learning problem and understand how it is evaluated.

Regression and Clustering

• Understanding of the basic concepts behind regression and clustering.

• Understanding of how these techniques can be applied for mood and emotion analysis of music signals.

Authenticity of this Certificate of Accomplishment can be verified at: https://www.kadenze.com/certificates/verified/S52B9ZSD

To receive a Certificate of Accomplishment, participants must meet all course requirements and be able to demonstrate required knowledge and skill through online assessments. Verified: To ensure academic integrity, the identity of all registered members is verified by Kadenze on an ongoing basis. Please note: Some online courses may draw on material from courses taught on-campus but they are not equivalent to on-campus courses. Participants who are currently enrolled or have completed courses offered by Kadenze or any Kadenze institutional partner are not to be considered registered students of the issuing universities. This statement does not affirm that this participant was enrolled as a student at University of Victoria in any way. It does not confer a University of Victoria grade, course credit, or degree, and University of Victoria has not verified the identity of the participant.

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Learning Outcomes