The curriculum runs over 3 semesters (18 months) and consists of 14 modules: 10 basic and 4 advanced. You follow the basic modules during the first two semesters and the advanced modules in the final semester.
Along with your studies, you pursue a work-based learning for the company.
The 90 ECTS credits of this MSc are distributed as follows:
This course covers Linear Algebra from basic matrix/vector operations to singular value decomposition and probabilities from fundamental basics to Markov chains and limit theorems, which are prerequires for most of the AI courses.
The course will be directed by examples and intuition rather than formalism. Python language will be used in examples and exercises. Octave (matlab) equivalent will also be available for the linear algebra part.
Although this course covers most of the basics, it is assumed students have some notion and background in linear algebra, probability and coding.
Labs will be application exercises (numeric or not) and exercises aiming at introducing aspects or notions that are not discussed in the course.
The course gives global knowledge in data structure and algorithms. It is organized in 4 parts:
2. Data structures and algorithms
3. Advanced algorithms
4. Computing tools
1. Signals and Signal Processing
Classification of Signals
Simple Time-Domain Operations, Filtering, sampling
2. Discrete-Time Signals and Systems
Sampling Rate Alteration
3. Discrete-Time Fourier Transform
The Continuous-Time Fourier Transform
The Discrete-Time Fourier Transform
Discrete-Time Fourier Transform Theorems
Digital Processing of Continuous-Time Signals
4. Discrete-Time Systems
Discrete Time System Examples
Classification of Discrete Time Systems (FIR/IIR)
5. Finite-Length Discrete Transforms
The Discrete Fourier Transform
Relation Between the Fourier Transform and the DFT and Their Inverses
DFT properties and theorems
Computation of the DFT of Real Sequences
Linear Convolution Using the DFT
Computation of the Convolution Sum of Finite-Length Sequences
The Transfer Function
Transfer Function Expression, relation to frequency response
7. LTI Discrete-Time Systems in the Transform Domain
characterization of LTI systems, stability
8. DSP Algorithm Implementation
Computation of the Discrete Fourier Transform
Splines and wavelets • Multirate Filter Banks and Wavelets
Fawwaz T. Ulaby and Andrew E. Yagle, “Signals and Systems,” ISBN: 978-1-60785-486-9 (harcopy)/978-1-60785-487-6 (electronic)
Alan V. Oppenheim, Ronald W. Schafer, “Discrete-Time Signal Pro-cessing (3rd Edition)” Prentice-Hall Signal Processing Series, ISBN-13: 978-0131988422, ISBN-10: 0131988425
Sanjit Mitra, “Digital Signal Processing,” Mcgraw Hill Higher Education; 4th edition (2010), ISBN10: 0071289461, ISBN-13: 978-0071289467
Martin Vetterli, Jelena Kovacevic, Vivek K. Goyal “Foundations of Sig-nal Processing” 3rd Edition ISBN-
10: 9781107038608, ISBN-13: 978-1107038608
The syllabus is divided into five topics each spanning two weeks. The first two topics are introductory, covering basic statistical modelling. The three subsequent ones are more involved, covering the estimation of parameters (the core of machine learning), inference (the basics of AI), and finally testing.
Topic 1: Discrete distributions
Introduction to the course, it’s teaching staff and structure
Origins and characteristics of some important discrete distributions
Introductory exercises and python notebook based labs
Topic 2: Continuous distributions
Derivation of some important continuous distributions
Introduction to Bayesian concepts including conjugacy
Conclusion of the first two “introductory” topics
Topic 3: Estimation
Minimum mean squared error
Maximum likelihood and Maximum a-Posteriori concepts
Topic 4: Inference and priors
Bayesian inference and the predictive distribution
The non-informative (Jeffreys) prior
Beta credible interval
Topic 5: Testing
Tests based on a normal assumption
Approximating non-normal cases
AI and the Law
AI and Data Protection
AI and Ethics
Reproducibility, What is it?
Data Organization and Evaluation
Version Control with git
Code Sharing with GitLab
Unit Testing and Continuous Integration
Documentation and Reporting
Packaging and Deployment
This class covers basic concepts in image and video processing as well as computer vision. Topics include image formation and sampling, image transforms, image enhancement, and image and video compression. Computer vision topics include points of interest, optical flow, and camera calibration.
• Introduction to Digital Image processing (imaging types and
• Point operations, image histograms
• Spatial Filtering and convolutions
• Edge detection
• 2D Fourier Transforms and representation of images, sampling, and image resizing (low pass filters, pyramids)
• Color images and color transformations
• Interest points (detection, representation, invariance, matching, RANSAC...)
• Optical Flow
• Andrew E. Yagle and Fawwaz T. Ulaby “Image Processing for Engineers,” Michigan Publishing, Ann Arbor, Michigan, 2018, ISBN: 978-1-60785-488-3 (hardcopy), 978-1-60785-489-0 (electronic)
• Rafael C. Gonzalez and Richard E. Woods, “Digital Image Processing Third Edition,” Pearson, ISBN 9780131687288, 2008
• Dimensionality Reduction and Clustering
• Kernel Methods and Support Vector Machines
• Graphical Models
• Exact and Approximate Inference in Bayesian Networks
• Probability Distribution Modelling
C. Bishop, “Pattern Recognition and Machine Learning”, Springer, 2006.
This course will introduce the students the fundamentals of speech processing and provide them with the key formalisms, models and algorithms to implement speech processing applications such as, speech recognition, speech synthesis, paralinguistic speech processing, multichannel speech processing.
why speech processing? speech production, speech perception, basic
Speech signal analysis
Sampling, Quantization, Time domain processing, Frequency domain
processing, linear prediction, cepstrum, speech coding
Practical: Speech signal analysis in Octave and Praat
Machine learning for speech processing
Static classification, Sequence classification, Regression
Practical: Statistical pattern recognition, Hidden Markov models in Octave
Automatic speech recognition
Dynamic programming, Instance-based speech recognition, Hidden
Markov model-based speech recognition, Evaluation measures
Practical: Kaldi tutorial
Concatenative speech synthesis, Statistical parametric speech synthesis, Evaluation measures
Practical: Grapheme-to-phoneme conversion, HMM-based speech synthesis
Paralinguistics speech processing
Emotion, gender, accent, pathological speech assessment, Evaluation
Practical: OpenSMILE tutorial
1. B. Gold, N. Morgan and D. Ellis, ``Speech and Audio Signal Processing", Wiley Publications, 2011.
2. P. Taylor, ``Text-to-Speech Synthesis", Cambridge University Press, 2009.
3. X. Huang, A. Acero and H-W. Hon, ``Spoken Language Processing: A Guide to Theory, Algorithm and System Development", Prentice Hall, 2001.
4. B. Schuller and A. Batliner, ``Computational Paralinguistics: Emotion, Affect and Personality in Speech and Language", Wiley, 2013.
Software tools: Octave, Praat, Kaldi, HTS, OpenSMILE (needed for practical)
The “deep learning” course aims at providing an overview of the deep learning area (theory, methods, applications, and tools) to the students and to use the most important tools via practical sessions.
Bias-variance dilemma through k-NN and polynomial fitting, mini deeplearning framework in numpy, PyTorch basics, MLP on MNIST and CIFAR, LeNet5 on MNIST and CIFAR, optimisation algorithms, finetuning on a pre-trained network, creation and training of model for person detection from top-view depth images, YOLOv3 for object detection, adversarial examples, distribution of activation maps as embeddings.
The module provides the student with project planning skills. The goal is to define the project(s) that will be developed in module P02. It can be divided in 6 parts:
• Familiarize with and understand your company, its corporate culture and strategy.
• Determine how the artificial intelligence takes part in your com-pany’s strategy
• Analyze state-of-the-art development of AI in the field of inter-est
• Design proof(s) of concept to guarantee the success of your project(s)
• Set a roadmap to plan your project(s) development
• Define the terms of reference of your project(s)
The aim of Module P02-AI Project(s) development is to develop the project(s) the student defines in Module P01 – AI Company strategy and Project(s) definition.