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Accueil > Formations > Master MVA > *Présentation des cours*

## Graphs in machine learning

### Lecturer : Michal VALKO, (INRIA Lille - Nord Europe, Sequel team)

### 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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