Academic courses

Our researchers provide academic courses at different levels: Bachelor, Master, and Doctoral. We have agreements with multiple institutions such as EPFL, University of Geneva, University of Lausanne or Mines-Paris Tech among others. The list of courses provided by Idiap membersexcluding those from the dedicated master in AI available hereis available below:


Bachelor / Master courses

Apprentissage et intelligence artificielle [Fundamentals of machine learning] (EE-311) Lecturer(s): Michael Liebling

Ce cours présente une vue générale des techniques d'apprentissage automatiques, passant en revue les algorithmes, le formalisme théorique, et les protocoles expérimentaux.

Where: EPFL Language: French

Automatic Speech Processing (EE-554) Lecturer(s): Mathew magimai Doss

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).

Where: EPFL Language: English

Computational Social Media (DH-500) 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.

Where: EPFL 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.

Where: EPFL Language: English

Image processing II (MICRO-512) Lecturer(s): Liebling Michael, Sage Daniel, Unser Michaël, Van De Ville Dimitri Nestor Alice

Study of advanced image processing; mathematical imaging. Development of image-processing software and prototyping in JAVA; application to real-world examples in industrial vision and biomedical imaging.

Where: EPFL Language: English

Statistical, geometrical and dynamical representations of movement (M2C7) Lecturer(s): Sylvain Calinon

This course will present various ways of representing movement data and gestures in a mathematical manner, with the goal of analyzing, compressing or generating movements. Several examples of applications will be covered, from generation of manipulation skills in robotics to the analysis of motion capture data.

Where: Mines-ParisTech Language: English

Human-Robot Interaction and Collaborative Robotics (M4C1) Lecturer(s): Sylvain Calinon

This course presents the use of artificial intelligence and machine learning techniques in human-robot interaction applications. In particular, it will focus on techniques to transfer skills by demonstration, inspired by imitation mechanisms to teach new skills to robots with an intuitive interface for the end-user.

Where: Mines-ParisTech Language: English

Biometrics (School of Criminal Justice (ESC)) Lecturer(s): Sébastien Marcel

This course introduces to the analysis, modelling and interpretation of biometric data for biometric person recognition, forensic biometrics, cybersecurity and behavioural biometrics in man-machine communication.

Where: University of Lausanne Language: French


Doctoral courses - Electrical Engineering

Perception and learning from multimodal sensors (EE-623) Lecturer(s): Odobez Jean-Marc

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.

Where: EPFL Language: English

Digital Speech and Audio Coding (EE-719) Lecturer(s): Magimai Doss Mathew, Motlicek 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.

Where: EPFL Language: English

Fundamentals in statistical pattern recognition (EE-612) Lecturer(s): Anjos André, Marcel Sébastien, Canévet Olivier
De Freitas Pereira Tiago

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).

Where: EPFL Language: English

Machine Learning for Engineers (EE-613) Lecturer(s): Calinon Sylvain, Fleuret François, Odobez 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. Laboratories will be done in python using jupyter notebooks.

Where: EPFL Language: English

Deep Learning For Natural Language Processing (EE-608) Lecturer(s): James Henderson
The Deep Learning for NLP course provides an overview of neural network based methods applied to text. The focus is on models particularly suited to the properties of human language, such as categorical, unbounded, and structured representations, and very large input and output vocabularies.
Where: EPFL Language: English


Thanks to an agreement with EPFL’s EDEE and EDIC Doctoral Programs, we fund and supervise a large number of PhD students (35 per year on average). The Institute also hosts international master and intern students. You can find open positions (PhDs, internships) currently available at Idiap on our job openings page.