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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 | ||