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Accueil > Formations > Master MVA > Présentation des cours
Graphs in machine learning
Lecturer : Daniele CALANDRIELLO and Michal VALKO, (INRIA Lille - Nord Europe, Sequel team)
Cours en anglais
Objective of the course :
The graphs come handy whenever we deal with relations between the objects. This course, focused on learning, will present methods involving two main sources of graphs in ML: 1) graphs coming from networks, e.g., social, biological, technology, etc. and 2) graphs coming from flat (often vision) data, where a graph serves as a useful nonparametric basis and is an effective data representation for tasks as spectral clustering, manifold or semi-supervised learning. We will also discuss online decision-making on graphs, suitable for recommender systems or online advertising. Finally, we will always discuss the scalability of all approaches and learn how to address huge graphs in practice. The lectures will show not only how but mostly why things work. The students will learn relevant topics from spectral graph theory, learning theory, bandit theory, necessary mathematical concepts and the concrete graph-based approaches for typical machine learning problems. The practical sessions will provide hands-on experience on interesting applications (e.g., online face recognizer) and state-of-the-art graphs processing tools (e.g., GraphLab).
Topics :
spectral graph theory, graph Laplacians and spectral clustering
constructing graphs from flat data - graphs as a non-parametric basis
semi-supervised learning and manifold learning
learnability on graphs - transductive learning
adaptive online learning with graphs
real-world graphs scalability and approximations
large-graphs, approximation, sparsification and error analysis
decision-making on graphs, graph bandits
social networks and recommender systems applications
vision applications (e.g., face recognition)
Prerequisites :
linear algebra, basic statistics, others tools needed will be covered in the lectures.
Organization of courses :
- 8 lectures, 2 hours long
- 3 recitations (TP/TD), 2 hours long
Validation :
Class project (60%) and short reports from the recitations (40%). The project could consist of reading the research papers, implementation, or theory of graph-based machine learning algorithms, etc. The project will be evaluated based on a short report and a presentation.
Rattrapage :
To be eligible for rattrapage, the students need to 1) submit at least 2 homeworks on their deadlines and receive at least one third of the maximum score from homeworks 2) submit the project report on the deadline and receive at least one third of the maximum score from the project report.
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