Programme of the Master in Artificial Intelligence

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 5 parts:

1. Introduction

2. Data structures and algorithms

3. Practical use of data formats

4. Advanced algorithms

5. Computing tools

• 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

• Linear regression

• Logistic Regression

• Decision Trees

• Boosting

• Multi-layer Perceptron

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

formats, applications)

• 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...)

• Calibration

• Optical Flow

• Dimensionality Reduction and Clustering

• Kernel Methods and Support Vector Machines

• Graphical Models

• Exact and Approximate Inference in Bayesian Networks

• Probability Distribution Modelling

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.

Course content

Introduction

why speech processing? speech production, speech perception, basic

phonetics

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

Text-to-speech synthesis

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

measures

Practical: OpenSMILE tutorial

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.