Courses Signal processing (FR)In this course, we introduce the main methods in signal processing.Automatic speech processing (EN)The goal of this course is to provide the students with the main formalisms, models and algorithms required for the implementation of advanced speech processing applications (involving, among others, speech coding, speech analysis/synthesis, and speech recognition, speaker recognition).Computational Social Media (EN)The course integrates concepts from media studies, machine learning, multimedia, and network science to characterize social practices and analyze content in platforms like Twitter/X, Instagram, YouTube, and TikTok. Students will learn computational methods to understand phenomena in social media.Machine Learning for Engineers (EN)The objective of this course is to give an overview of machine learning techniques used for real-world applications, and to teach how to implement and use them in practice. Laboratories will be done in python using jupyter notebooks.Fundamentals in statistical pattern recognition (EN)This course provides in-depth understanding of the most fundamental algorithms in statistical pattern recognition or machine learning (including Deep Learning) as well as concrete tools (as Python source code) to PhD students for their work. Digital Speech and Audio Coding (EN)The goal of this course is to introduce the engineering students state-of-the-art speech and audio coding techniques with an emphasis on the integration of knowledge about sound production and auditory perception through signal processing techniques.Perception and learning from multimodal sensors (EN)The course will cover different aspects of multimodal processing (complementarity vs redundancy; alignment and synchrony; fusion), with an emphasis on the analysis of people, behaviors and interactions from multimodal sensor, using statistical models and deep learning as main modeling tools.Deep Learning For Natural Language Processing (EN)This course covers advanced topics in deep learning architectures for natural language processing. The focus is on attention-based architectures, structure processing and variational-Bayesian approaches, and why these models are particularly suited to the properties of human language.Signals and systems (for EL) (FR)This course establishes the foundations of an essential concept in engineering: the notion of a system. More specifically, the course introduces the theory of linear time-invariant (LTI) systems, which are widely used to model both physical reality and human-engineered systems.Genomics and bioinformatics (EN)This course covers various data analysis approaches associated with applications of DNA sequencing technologies, from genome sequencing to quantifying gene evolution, gene expression, transcription factor binding and chromosome conformation.Image analysis and pattern recognition (EN)This course gives an introduction to the main methods of image analysis and pattern recognition. Lab in signal and image processing (EN)These lab sessions are hands-on exercises focusing on the basics of image processing and deep learning. The main objective is to learn how to use some important image processing libraries, namely OpenCV, numpy and TensorFlow, to perform image analysis tasks.Lab in information technologies (FR)Get familiar with experimental aspects of the main domains of the orientation “Information and communication technologies”Fundamentals of electrical circuits and systems I (EN)This course gives you an introduction to signal processing, focusing on the Fourier transform, on signal sampling and reconstruction and the Discrete Fourier transform.
