Professor: Philippe Esling
Lessons • Setup • Administrative • Detailed lessons • Contribution • About
This repository contains the courses in machine learning applied to music and other creative mediums. This course is currently given at the University of Tokyo (Japan), and along the ATIAM Masters at IRCAM, Sorbonne Paris (France). The courses slides along with a set of interactive Jupyter Notebooks will be updated along the year to provide all the ML program. This course is proudly provided by the ACIDS group, part of the Analysis / Synthesis team at IRCAM. This course can be followed entirely online through the set of Google slides and Colab notebooks links provided openly along each lesson. However, we do recommend to fork the entire environment and follow the interactive notebooks through Jupyter lab to develop your own coding environment.
As the development of this course is always ongoing, please pull this repo regularly to stay updated. Also, please do not hesitate to post issues or PRs if you spot any mistake (see the contribution section).
Join the Discord channel of this course to join the community and share experiences and problems.
Table of Contents
Quick explanation. For each of the following lessons, you will find a set of badges containing links to different parts of the course, which allows you to follow either the online or offline versions.
- Online:
- Offline:
Simply click on the corresponding badge to follow the lesson. Note that if the badge is displayed in red color as follows
This course provides a brief history of the development of artificial intelligence and introduces the general concepts of machine learning through a series of recent applications in the creative fields. This course also presents the pre-requisites, course specificities, toolboxes and tutorials that will be covered and how to setup the overall environment.
This course introduces the formal notions required to understand machine learning along with classic problems of linear models for regression and classification. We discuss the mathematical derivation for optimization and various problems of overfitting, cross-validation and model properties and complexity that are still quintessential in modern machine learning.
This course provides a brief history of the development of neural networks along with all mathematical and implementation details. We discuss geometric perspectives on neurons and gradient descent and how these interpretation naturally extend to the case of multi-layer perceptrons. Finally, we discuss the complete implementation of backpropagation through micro-grad.
In this course we introduce more advanced types of neural networks such as convolutional and recurrent architectures, along with more advanced models (LSTM, GRU) and recent developments such as residual architectures. We further discuss issues of regularization and initialization in networks.
We introduce here the fundamental shift towards deep learning, notably through the development of layerwise training and auto-encoders. We discuss how these are now less relevant through novel regularization methods and data availability. We finish this course by discussing the recent attention mechanism and transformer architectures and provide a set of modern applications.
To operate the shift towards generative models, we introduce here the fundamentals of probabilities, distribution and inference. We discuss several properties and introduce Bayesian inference by developing the mathematical foundations of Maximum A Posteriori (MAP) and Maximum Likelihood (ML) techniques.
We discuss the distinction between supervised and unsupervised learning, through the first context of clustering. This allows to introduce the notion of latent variables and how we can solve for this using Expectation-Maximization (EM). We provide the full derivation through Variational inference and discuss the implementation of Gaussian Mixture Models (GMM)
We introduce here the notion of approximate inference, through historical applications of sampling with Monte-Carlo and Metropolis-Hastings. We further show how variational inference can provide an elegant solution to our estimation problems and discuss its implications.
We show how fusing Auto-Encoders and variational inference leads to the Variational Auto-Encoder (VAE), a powerful generative models. We discuss issues of disentanglement and posterior collapse and introduce the recent Normalizing Flows and how these can be used in generative contexts.
Additional notebook on Normalizing Flows
We introduce the notion of estimating by comparing and derive the complete adversarial objective naturally from this observation. After discussing adversarial attacks, we introduce Generative Adversarial Networks (GANs), which are still competitive generative models to this day. We discuss flaws and limitations of GANs and introduce their modern applications.
This course explores the new class of generative models based on diffusion probabilistic models. This class of models is inspired by thermodynamics, but also denoising score matching, Langevin dynamics and autoregressive decoding. We will also discuss the more recent development of denoising diffusion implicit models and the wavegrad model, which is based on the same core principles but applies this class of models for audio data.
This year we are very happy to host two special guest lectures that are aimed to provide an advanced view on both the academic and industrial research currently undergoing in creative machine learning.
Along the tutorials, we provide a reference code for each section. This code contains helper functions that will alleviate you from the burden of data import and other sideline implementations. You will find designated spaces in each file to develop your solutions. The code is in Python (notebooks impending) and relies on the concept of code sections, which allows you to evaluate only part of the code (to avoid running long import tasks multiple times and concentrate on the question at hand.
Please refer to the setup notebook to check if your configuration is correct
In order to get the baseline scripts and notebooks to work, you need to have a working distribution of Python 3.7 as a minimum (we also recommend to update your version to Python 3.9). We will also be using a large set of libraries, with the following ones being the most prohiminent
We highly recommend that you install Pip or Anaconda that will manage the automatic installation of those Python libraries (along with their dependencies). If you are using Pip, you can use the following commands
pip install -r requirements.txt
If you prefer to install all the libraries by hand to check their version, you can use individual commands
pip install numpy
pip install scikit-learn
pip install torch
pip install jax
pip install librosa
pip install matplotlib
For those of you who have never coded in Python, here are a few interesting resources to get started.
In order to ease following the exercises along with the course, we will be relying on Jupyter Notebooks. If you have never used a notebook before, we recommend that you look at their website to understand the concept. Here we also provide the instructions to install Jupyter Lab which is a more integrative version of notebooks. You can install it on your computer as follows (if you use pip)
pip install jupyterlab
Then, once installed, you can go to the folder where you cloned this repository, and type in
jupyter lab
The notebooks will be evaluated through rolling deadlines that happen at 3 sessions apart. Hence notebook for course 01 (Machine Learning) must be submitted before the beginning of course 04 (Deep Learning), and that of course 02 before course 05, and so forth.
Submission instructions:
Please add your informations for enrolling to the evaluation system by adding a row in the following sheet
Once enrollment has been confirmed, you can register and submit your notebooks on the following [web interface][https://okpy.org/atiam/cml/au23/] https://okpy.org/atiam/cml/au23/
Assignments will be updated on a weekly basis.
These administrative details concerns only the current (spring) physical session attached to the University of Tokyo (Spring session)
| Type | Information |
|---|---|
| Type | 2-credits graduate school course |
| Period | April - July 2023 |
| Span | 13 classes of 105 minutes |
| Date | Thursday at 2:55 - 4:40 pm (JST Time) |
| Onsite | Room 214, 2nd Floor, Sci. 7 Building |
| Online | https://u-tokyo-ac-jp.zoom.us/j/81363691008?pwd=cmxiRURMdm9udXBKbTNjQkZvblNFQT09 |
| Date | Course |
|---|---|
| April 11 | 00 - Introduction |
| April 18 | 01 - Machine learning |
| April 25 | 02 - Neural networks |
| May 2 | 03 - Advanced networks |
| May 9 | 04 - Deep learning |
| May 16 | 05 - Bayesian inference |
| May 23 | 06 - Latent models |
| May 30 | No course |
| June 6 | 07 - Approximate inference |
| June 13 | 08 - VAEs and flows |
| June 20 | 09 - Adversarial networks |
| June 27 | 10 - Diffusion models |
| July 4 | 11 - Guest lecture #1 |
These administrative details concerns only the current (autumn) physical session attached to the ATIAM Masters (IRCAM)
| Type | Information |
|---|---|
| Type | Masters (3-ECTS) school course |
| Period | September - December 2023 |
| Span | 7 classes of 200 minutes (sessions of 55 minutes) |
| Date | Variable slots (check calendar) |
| Onsite | Room Stravinsky, IRCAM, -2 |
| Online | N / A |
| Date | Course |
|---|---|
| November 10 | 00 - Introduction |
| November 10 | 01 - Machine learning |
| November 13 | 02 - Neural networks |
| November 13 | 03 - Advanced networks |
| November 20 | 04 - Deep learning |
| November 30 | 05 - Bayesian inference |
| November 30 | 06 - Latent models |
| December 08 | 07 - Approximate inference |
| December 08 | 08 - VAEs and flows |
| December 11 | 09 - Adversarial networks |
| December 18 | 10 - Diffusion models |
Please take a look at our contributing guidelines if you're interested in helping!
Code and documentation copyright 2012-2023 by all members of ACIDS.
Code released under the CC-BY-NC-SA 4.0 licence.