Early 2018

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Length
4 Sessions
Price
Institution
University of Victoria
Subject
Music, Creative Computing, Music Technology
Skill Level
Expert
Topics
Music, Music Information Retrieval, Audio Signal Processing, Data Mining
Course Description

An introduction to data mining through the lens of music information retrieval. Topics explored include classification (genre, mood, instrument), multi-label classification (tagging), and regression (emotion/mood).

schedule

This course is in scheduled mode and starts Early 2018. Learn more about scheduled courses here.

Session 1: Naive Bayes classification
In this session, we will learn about the main idea of generative classifiers using probabilistic modeling, Bayes theorem, the naive bayes assumption, evaluation of classification, cross-validation.
Session 2: Discriminating classifiers
Decision trees, perceptron, artificial neural networks, support vector machines will be covered in this session.
Session 3: Tagging
This session is about methods of tag acquisition (surveys, games with a purpose), auto-tagging architectures, evaluation of auto-tagging.
Session 4: Regression
We will learn about Regression and how it is applied in emotion/mood recognition, and other regression applications such as surrogate sensing for music instruments.
Enroll for College Credit

Credit Eligible

Kadenze has partnered with University of Victoria to offer this program for 3.0 college credits.*

How much does it cost?

This course costs $900 USD to take for college credit.

*Upon completion, this rigorous college-level program will provide credits that are recognized and transferable from the partnering institution. Credit as workload and transferability is defined by the granting institution. Participation in these courses does not represent an acceptance decision or admission from the institution that offers them.

Learning Outcomes

Below you will find an overview of the Learning Outcomes you will achieve as you complete this course.

Certificates
Whenever you complete a course as a premium member, you can earn a verified Certificate of Accomplishment .

This course is also part of the Program: Music Information Retrieval . Earn a verified Specialist Certificate for successfully completing a Program.

These certificates are proof that you completed an online course on our platform and can easily be shared with its unique link.

Credit Elligible Program
This course is one of 3 courses in the Music Information Retrieval Program and is offered for credits from University of Victoria.

Earn a verified Specialist Certificate after successfully completing a Program. And whenever you complete a course as a Premium member, you earn a verified Certificate of Accomplishment . These certificates are proof that you completed an online course on our platform and can easily be shared with its unique link.

Instructors & Guests
What You Need to Take This Course

Prior Knowledge

  • Good knowledge of programming, basic linear algebra, probability, and statistics.

Equipment

  • Computer with installation privileges.

Software

  • The course is mostly software agnostic but existing frameworks for MIR and audio will be used. All software will be freely available and typically also open source. Examples include: Audacity, Marsyas, Sonic Visualizer, and VAMP plugins.

Prerequisite

  • "Extracting Information from Music Signals" must be completed prior to taking this course.
Additional Information

PLEASE NOTE: Taking part in a Kadenze course as a Premium Member does not affirm that you have been enrolled or accepted for enrollment by University of Victoria.

In order to receive college credit for these program courses, you must successfully complete and pass all 3 courses in this program. If a student signs up for the Music Information Retrieval program, it is recommended that these courses are taken sequentially.

*Partial credit will not be awarded for completion of only one course.

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Please understand that posts which violate this Code of Conduct harm our community and may be deleted or made invisible to other students by course moderators. Students who repeatedly break these rules may be removed from the course and/or may lose access to Kadenze.

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