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).
Overview
Syllabus
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.
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.
Taught by
George Tzanetakis
