Courses

Bachelor / Master courses

Automatic Speech Processing (EE-554) Lecturer(s): Bourlard Hervé

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).

Language : english

Deep Learning (EE-559) Lecturer(s): Fleuret François

The objective of this course is to provide a complete introduction to deep machine learning. How to design a neural network, how to train it, and what are the modern techniques that specifically handle very large networks.

Language : english

Probabilities and statistics (MATH-234(a)) Lecturer(s): Ginsbourger David

Ce cours est une introduction à la théorie des probabilités et de la statistique. Basé sur les concepts fondamentaux des probabilités il traite les notions d’inférence statistique et de régression linéaire simple et multiple.

Language : French

Doctoral courses – Electrical Engineering

Computational perception using multimodal sensors (EE-704) Lecturer(s): Odobez Jean-Marc

The course will cover perceptual modalities in computers, models for analyzing people (representation, detection an localization, segmentation, tracking, recognition).

Language : english


Computational Social Media (EE-727) Lecturer(s): Gatica-Perez Daniel

The course integrates concepts from media studies, machine learning, multimedia and network science to characterize social practices and analyze content in sites like Facebook, Twitter and YouTube. Students will learn computational methods to infer individual and networked phenomena in social media.

Language : english


Digital Speech and Audio Coding (EE-719) Lecturer(s): Magimai Doss MathewMotlicek Petr

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.

Language : english


Fundamentals in statistical pattern recognition (EE-612) Lecturer(s): Anjos AndréMarcel Sébastien

This course provides in-depth understanding of the most fundamental algorithms in statistical pattern recognition as well as concrete tools (as source code) to PhD students for their work. It will cover regression, classification (MLP, SVM) and probability distribution modeling (k-Means, GMM, HMM).

Language : english


Machine Learning for Engineers (EE-613) Lecturer(s): Calinon SylvainFleuret FrançoisOdobez Jean-Marc

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.

Language : english


Statistical Sequence Processing (EE-605) Lecturer(s): Bourlard Hervé.

This course discusses advanced methods extensively used for the processing, prediction, and classification of temporal (multi-dimensional and multi-channel) sequences. In this context, it also describes key links between signal processing, linear algebra, statistics and artificial neural networks.

Language : english