Advanced Deep Learning at Télécom Physique Strasbourg
Here are the lecture slides in pdf:
- cours_IA_avancé_TPS_animated.pdf (Animated)
- cours_IA_avancé_TPS_printable_1-per-page.pdf (1 slide per page)
- cours_IA_avancé_TPS_printable_4-per-page.pdf (4 slides per page)
Here are the materials for the turorial / lab sessions. These tutorials were originally created by Paul Magron. The .zip files are password protected.
- General instructions (in French): Instructions TP Deep Learning
- Instructions for setup on Linux: setup.zip
- Datasets: data.zip
- Lab1: lab1.zip
- Lab2: lab2.zip
- Lab3: lab3.zip
- Lab4: lab4.rar
- Lab5: lab5.zip
- Lab6: lab6.zip
Intelligent Sensing Winter School 2021
On December 9th 2021, I gave a virtual lecture entitled « Machine Learning for Indoor Acoustics » at the Intelligent Sensing Winter School of the Queen Mary University of London. This year’s themes were « AI for sound perception, AI for visual perception, AI for multimodal perception ». You can download the slides here: QMUL_CIS_Winter_School_deleforge_09.12.2021_split.pdf.
IEEE S3P 2019 Summer School in Arenzano
On September 13th 2019, I gave a lecture entitled « Taking the Best of Physics and Machine Learning in Robot Audition » at the IEEE S3P 2019 Summer School in Arenzano, Italy. This year’s theme was « Signal Processing for Autonomous Systems ». You can download the slides here: S3P2019_deleforge_animated.pdf.
Master ISTIC – Module VAI
- October the 3rd and the 4th 2017 : Here are the slides (In English) of my lecture on Auditory Scene Synthesis and Analysis (Spatialization, Localization, Separation) : cours_ISTIC_casa_2017_english.pdf
Bayesian Learning for Signal Processing
On August the 24th 2015, Mikkel Schmidt and I were invited to give a tutorial on Bayesian Learning for Signal Processing at LVA/ICA 2015’s summer school in Liberec (Czech Republic). Here are the slides of the two parts I presented
- Bayesian Inference (or The adventures of Sir Thomas Bayes!) : bayesian_inference_electronic.pdf
- Bayesian beamforming and multichannel Wiener filtering:
bayesian_multichannel_and_conclusion_electronic.pdf
Unsupervised Learning
This is a series of four 3-hour long practical sessions on unsupervised learning taught at the Engineering school Télécom Physique Strasbourg (2021 – now).
- Slides of the Lecture
- Exercice 3: Clustering
- Exercice 4: Dimensionality Reduction
- Exercice 5: Dictionary Learning (my_ksvd.m)