# Artificial Intelligence, Machine Learning, Deep Learning

This is a series of lectures I am giving in 2023 at Télécom Physique Strasbourg on A.I., Machine Learning and Deep Learning. Here is the material for the class:

**Animated versions**:

- Part I – Introduction (AI, Machine Learning, Deep Learning): cours_IA_TPS_chap1.pdf
- Part II – Background (Linear Algebra, Differential Calculus): cours_IA_TPS_chap2.pdf
- Part III – Fitting a Model (Optimization, Gradient Descent, Backprop, PyTorch): cours_IA_TPS_chap3.pdf
- Part IV – Supervised Learning (Regression, Classification, Choosing the loss, Over/Underfitting): cours_IA_TPS_chap4.pdf
- Part V – Unsupervised Learning (Clustering, Dimensionality Reduction): cours_IA_TPS_chap5.pdf
- Part VI – Convolutional Neural Networks (What, Why, Layers, Examples): cours_IA_TPS_chap6.pdf

**Printable version of the full course**: cours_IA_TPS_full_printable.pdf

**Tutorials** (password protected):

- Instructions for setup: 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)