This section summarizes the courses in machine learning applied to music computing given along the ATIAM Masters at IRCAM.

# Evaluation

The evaluation of the ML course is defined by a group project. All details can be found on the corresponding projects page

# Course summary

## Introduction

- Introduction to artificial intelligence
- Properties of machine learning
- Nearest-neighbors

## Neural Networks

- The artificial neuron
- Neural networks
- Architecture zoo

## Support Vector Machines

- Support Vector Machines
- Properties of kernels

## Unsupervised clustering

- Clustering motivations
- K-Means and k-medoids
- Hierarchical clustering

## Meta-heuristics

- Genetic algorithms
- Boosting

## Probabilistic models

- Probabilities and distributions
- Belief networks

## Bayesian inference

- Bayesian learning
- Undirected graphical models
- Maximum likelihood

## Gaussian Mixture Models

- Expectation Maximization
- Mixture models

## Hidden Markov Models

- Undirected graphical models
- Hidden Markov models

## Data complexity and GPU computing

- Pitfalls of machine learning
- Data complexity
- GPU computing

## Deep learning

- Deep learning
- Applications

## Going further

- Applications of ML
- Current trends

# Tutorials

The present tutorials covers coding exercices designed to implement the core notions seen in the machine learning lessons. Most techniques can be applied to any type of data from which sets of features can be computed. The exercices here target these techniques specifically applied to musical or audio data.

Get the baseline code for your language of choice :

Get the audio datasets from this link

**Unzip files and place 00_Datasets along with other folders**

# Exercices

# References

- Linear algebra revision slides from Andrew Ng
- Probability revision slides from Andrew Ng
- Statistics course notes from William Faris
- Sampling pages 20 to 31 from Iain Murray
- Matrix calculus in the Matrix Cookbook