Michal Valko : Graphs in Machine Learning - Spring 2021

Graphs in Machine Learning - Spring 2021 - MVA - ENS Paris-Saclay

News

  • 02.10.2020: The main instructor 2020/2021 will be Daniele Calandriello from DeepMind Paris.
  • 02.10.2020: First class will start on 05. 01. 2021 at 13h30.
  • 08.09.2020: Changes 2020/2021, please contact Daniele Calandriello for any information about this course.
old news

Administrivia

  • Time: Tuesdays 13:30
  • Place: ENS Paris-Saclay - Amphitheater 1Z18
  • 7 or 8 lectures and 3 recitations (TD)
  • Validation: grades from TD (40%) + class project (60%)
  • Research: projects, internships (stages) and PhD. thesis at SequeL and elsewhere possible
  • Piazza: Registration (with your school email) and online class discussion on piazza.
  • TA: Omar Darwiche Dominques
  • course description at MVA at ENS Paris-Saclay
  • MVA tags: content: #apprentissage, type: #méthodologique #théorique, validation: #projet #td

Main topics

  • spectral graph theory, graph Laplacians
  • semi-supervised graph-based learning
  • manifold learning
  • graphs from flat data - graph as a non-parametric basis
  • online learning with graphs
  • real world graphs scalability and approximations
  • graph neural networks
  • social networks and recommender systems applications
  • large graph analysis, learning, and mining
  • vision applications (e.g., face recognition)

Important: Don't take this class ...

  • ... if you don't have time to do reports. While 5-10% students finish their 3 assignments during the 2-hour long recitations, about 20% students find that they spend 3 times longer time doing homework reports than for other classes in the master program.
  • ... if you expect your instructor to reply to your emails or not willing to read this webpage and other instructions. Piazza is the place for all communication.
  • ... if you believe that extra extensions beyond the rules below would be granted or if you cannot deliver the project report on time.

Intro

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 such 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, graph neural networks, 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.

Organization

The course will feature 11 sessions, 8 lectures and 3 recitations (TD), each of them 2 hours long. There may be a special session with guest lectures. There may be also an extra homework with extra credit. The evaluation is be based on reports from TD and from the projects. Several project topics will be proposed but the students will be able to come up with their own and they will be able to work in groups of 2-3 people. The best reference for this course are the slides from the lecture which are made to be comprehensive and there is no recommended textbook. The material we cover is mostly based on research papers, some of which very recent. The course will be in English.

Recitations and homeworks (TDs)

Bring your own laptop to the practical sessions. Each of the 3 practical sessions are followed by a graded report. The assignments are posted on piazza. You are welcome to discuss with your peers (in which case indicate the people you have discussed with in your report), but the reports should be written individually to avoid a penalty.

Class projects

The main part the of the grade comes from the projects. The students are encouraged to come up with the topic of their interest related to the course and start working on it as soon as possible. In this case, please e-mail the lecturer with a short description of the project for the approval. Some project proposals will be given. Additional project proposals will be presented on XX. YY. 2021. Deadline for deciding on the project is XX. YY. 2021, but the recommended date for picking up the projects is on XX. YY. 2021. The deadline for submitting 5-10 page reports in NeurIPS format is XX. YY. 2021. The planned time for 15+5 minutes will be from XX. YY. 2021 over Skype/Hangout. Students can work in pairs of 2 and exceptionally 3. Very detailed instructions are given on the dedicated page for the class projects.

Registration, Communication, and Questions

We will be using piazza for the enrollment and online class discussion. Use your full name and your school e-mail when registering. The access code will be given out during the class. Piazza is the place of questions regarding lectures, homeworks, and logistics. Posting questions to piazza makes the whole class benefit from the answers and enables students to answer questions too. However, refrain from posting the solutions to the homeworks. Please use piazza also for public or private communication with the instructors of any kind. E-mails that should be posted to Pizza to the instructors will not be answered or will be answered late by a canned response "please post this question to piazza".

Late policy

You will have 4 late days without penalty to be used across the entire course. You can use them for any deadline (homework, project assignment, project report delivery). After those late days are used, you will be penalized according to the following policy: (1) full credit at the midnight on the due date, Paris time (2) half credit for the next 48 hours; (3) zero credit after that. We encourage the students not to use these late dates except in the exceptional circumstances. All the deadlines are strict and we ask students to avoid demanding the extensions. If you have serious reasons that prevent you meeting the deadlines, please use the formal procedures of your school.

Prerequisites

linear algebra, basic statistics, others tools needed will be covered in the lectures

Course page from previous years:

15-Oct-2020