Courses I took in M2 DATA-AI

The Machine Learning Pipeline Data Preprocessing and Exploration Feature Selection/Engineering & Dimensionality reduction Supervised Learning, Deep and Reinforcement Learning, Unsupervised Learning.
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Text preprocessing and Information Retrieval, graph-of-words, keyword extraction, Text categorization, topic modeling, supervised document classification, Word and document embeddings, unsupervised document classification with the Word Mover's Distance, Advanced deep learning architectures for NLP seq to seq tasks (HAN, ELMO, BERT/Transformer...), Lexical statistics and n-gram models, Sequence Labeling: Named Entity Recognition, POS-tagging, Introduction to Parsing, elements of Machine Translation, Semantics - Knowledge Bases, Relation Extraction.

This course first gives an overview of the field of data visualization. It then discusses fundamental principles of human visual perception, focusing on how they help inform the design of visualizations. The following sessions focus on visualization techniques for specific data structures, and discuss them in depth from both design and implementation perspectives, including: multi-variate data, hierarchical structures, networks, time-series, statistical data and geographical data. All exercises are based on Web technologies, including the D3 software library (Data-Driven Documents) and the Vega-lite interactive graphics grammar. While positioned at different levels of abstraction, both enable developers to create a wide range of interactive, Web-based visualizations that run on a variety of platforms, ranging from desktop workstations to mobile devices.
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This course aims at providing the bases of symbolic AI, along with a few selected advanced topics. It includes courses on formal logics, ontologies, symbolic learning, typical AI topics such as revision, merging, etc., with illustrations on preference modeling and image understanding.
During this course, we realized an ontology on Protégé which aims to classify place in different "risk" in case of Covid-19 propagation. Github repo.
Course webpage

This course will discuss the automated construction of large knowledge bases. For this, we will cover the basics of knowledge representation, natural language processing (POS tagging, dependency parsing), information extraction (fact extraction, named entity recognition), and rule mining and disambiguation. We will see both classical/symbolic methods and deep learning methods for these tasks.

This module will present the basis of architectures and algorithms for bigdata processing at a very large scale. It covers Map Reduce Apache Spark, Lambda and Kappa Architectures.
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This course deals with visual data (images and videos), and talks about image representation, processing and indexing, for content-based retrieval purposes. - It starts from image data and their different models, from mathematical and algorithms viewpoints, by exploring the different models: frequency-, discrete-, or set-based, differential, or statistical... - It presents segmentation and feature extraction techniques, i.e. how to reduce the representation support, and what local and global representations can be used to describe the image content. - Practical Work #1 deals with salient point detection, description and matching - Approximately on third of the course is dedicated to classification, detection and image recognition techniques based on machine learning, using CNN (one session) and other unsupervised and supervised techniques (one session). - Practical Work #2 deals with image classification using CNN. - One session is dedicated to a significant use case: satellite image mining. - One session is on video analysis and the importance of motion in video mining, with an emphasis on object tracking methods. - Practical Work #3 is on object tracking in videos. The practical works use Python, OpenCV and Pytorch. The evaluation is based on the 3 reports on the practical works (Weight 0.6), and a theoretical exam (Weight 0.4).
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Relational databases: ER modeling, SQL, query execution, query optimization, schema refinement, application programming
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Students learn how to give good presentations, and present scientific papers.
Research paper that I presented : VLog: A Rule Engine for Knowledge Graphs - Link of the presentation
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Algorithmic fairness, ethical issues, privacy and security

Introduction to multimodal human-agent dialogue. Emotional recognition, gesture recognition, speech synthesis, multimodal dialogue system, interaction analysis This course provides students with foundational conceptual knowledge, methodologies, and tools for designing, implementing, and evaluating intelligent machines able to engage users in a multimodal dialogue. This requires students to know and apply computational methods for capturing, representing, automatically analyzing the behavior of the users, and generating the behavior of the machines. At the end of the course, the student will: Understand the principles of multimodal communication and its open challenges; Know and understand the motivations for using multimodality for designing intelligent machines Know and understand computational methods for managing the dialogue through the following communication modalities: speech, movement, and facial expressions Know and understand the foundations of conversational analysis.

The course will cover several social phenomena, including: collective decision, the cocktail party effect, scale-free social networks, the hawk-dove dilemma, cooperation in insect societies, emergence of segregationism, altruism, the "tragedy of the commons", the "green-beard" effect, social coordination, suicide "for the group", honest communication, charity and competitive helping. Several theoretical models will be studied, including preferential attachment, kin selection, the Prisoner’s dilemma, the handicap principle, social signaling. Several of these models derive from applying Game Theory to social dilemma. Content: Emergence, swarm intelligence, genetic algorithms, genetic programming, morphogenesis, emergence of sociality.
During the week we implement a 2D Cellular Automaton algorithm on Evolife software. Presentation.
Course webpage

Self-adaptation, self-organisation, autonomic control, multi-agent systems: architectures, design patterns, service-oriented platforms, practical project based on smart-home simulator.

The course will provide an introduction to data mining and will cover the following topics: clustering, decision trees, ranking, association rules, recommendation systems, introduction to MapReduce and Spark. Students will work on a project where they will implement some of the previously mentioned algorithms in Python or in Spark.
During this course, I developped a Decision Tree algorithm from scratch. Github repo
Course webpage

Semantic networks, logic (logic of predicates, logiqe of description, ...), reasoning, ontologies discover Semantic Web languages (RDF, RDFS, OWL, SPARQL) TP (protégé, jena).

Bayes networks, hidden Markov models, theory of evidence, segmentation, filtering, smoothing. Examples of applications to image, finance, digital communications.