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esling:atiam-ml.html [2015/12/15 12:13] Philippe Esling [Part 4. Support Vector Machines] |
esling:atiam-ml.html [2016/08/16 13:21] (Version actuelle) Philippe Esling |
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- | ====== ATIAM Music machine learning ====== | + | ====== ATIAM - Music machine learning ====== |
This section summarizes the courses in machine learning applied to music computing given along the ATIAM Masters at IRCAM. | This section summarizes the courses in machine learning applied to music computing given along the ATIAM Masters at IRCAM. | ||
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===== Courses ===== | ===== Courses ===== | ||
- | === Supervised and AI === | + | ==== I - Introduction ==== |
** {{ml.cours.1.pdf|Slides}} ** | ** {{ml.cours.1.pdf|Slides}} ** | ||
- Introduction to artificial intelligence | - Introduction to artificial intelligence | ||
- Properties of machine learning | - Properties of machine learning | ||
+ | - Nearest-neighbors | ||
+ | ==== II - Neural Networks ==== | ||
+ | ** {{ml.cours.2.pdf|Slides}} ** | ||
+ | - The artificial neuron | ||
- Neural networks | - Neural networks | ||
- | === From supervised to unsupervised === | + | - Architecture zoo |
+ | ==== III - Support Vector Machines ==== | ||
** {{ml.cours.2.pdf|Slides}} ** | ** {{ml.cours.2.pdf|Slides}} ** | ||
- Support Vector Machines | - Support Vector Machines | ||
- Properties of kernels | - Properties of kernels | ||
- | - Clustering algorithms | + | ==== IV - Unsupervised clustering ==== |
- | === Probabilistic Graphical Models === | + | - Clustering motivations |
- | ** [[esling:slides.pdf|Slides]] ** | + | - K-Means and k-medoids |
- | - Probability and Bayesian inference | + | - Hierarchical clustering |
+ | ==== V - Meta-heuristics ==== | ||
+ | - Genetic algorithms | ||
+ | - Boosting | ||
+ | ==== VI - Probabilistic inference ==== | ||
+ | - Probabilities and distributions | ||
+ | - Belief networks | ||
+ | ==== VII - Bayesian inference ==== | ||
+ | - Bayesian learning | ||
+ | - Undirected graphical models | ||
+ | - Maximum likelihood | ||
+ | ==== VIII - Gaussian Mixture Models ==== | ||
+ | - Expectation Maximization | ||
+ | - Mixture models | ||
+ | ==== IX - Probabilistic Graphical Models ==== | ||
- Undirected graphical models | - Undirected graphical models | ||
- | - Expectation maximization | ||
- | === Advanced Models === | ||
- | ** [[esling:slides.pdf|Slides]] ** | ||
- | - Gaussian mixture models | ||
- Hidden Markov models | - Hidden Markov models | ||
+ | ==== X - Data complexity and GPU computing ==== | ||
+ | - Pitfalls of machine learning | ||
+ | - Data complexity | ||
+ | - GPU computing | ||
+ | ==== XI - Deep learning ==== | ||
- Deep learning | - Deep learning | ||
- Applications | - Applications |