Advanced Applied Data Analytics

Résumé de la formation

This course is the second part of the diiP course “Applied Data Analytics.” The first part covers basic techniques, methodologies, and practical skills for data analysis. The second part delves into more advanced topics in Machine Learning and Deep Learning (see schedule below).

Like the first part, this course is intended for a variety of doctoral students from several faculties at UPCité. Key topics include data science, data analysis, machine learning, deep learning, and data mining.

S'inscrire

Programme

Program Overview

 

 

  • The detailed program & Syllabus are accessible HERE

Monday

11/01/27

Tuesday

12/01/27

 Wednesday 13/01/27 Thursday 14/01/27

Friday

15/01/27

10h00-11h20

Introduction
Objective of the course (Review of some basic topics)

10h00-11h20

Transformers and Pre-Trained Models

10h00-11h20

Graph Neural Networks

10h00-11h20

Dimensionality Reduction

 

10h00-11h20

Knowledge-Informed Neural Networks

 

11h20-11h30

Break and

Poll ☕

11h20-11h30

Break and

Poll ☕

11h20-11h30

Break and

Poll ☕

11h20-11h30

Break and

Poll ☕

11h20-11h30

Break and

Poll  ☕

11h30-13h00

Generative Adversarial Networks, Autoencoders

11h30-13h00

Graph Neural Networks

 

11h30-13h00

Probabilistic Modelling

 

11h30-13h00

Hyperparameter Tuning

11h30-13h00

Knowledge-Informed Neural Networks

 

 

Formateurs et formatrices

Formateur : Yvonne Becherini

Email (contact pédagogique): becherini@u-paris.fr

Informations pratiques

Practical Information

 

Dates : Starting on the 12th of January 2026 until the 16th of January 2027
From 10 a.m to 1p.m

-→ This training takes place online via Zoom. The link will be send to you directly by the trainer.

 

Rules of Attendance

-→ This course takes 15 hours.

-→ Attendance is mandatory for at least 4 days of training.

You will get credited :

  • Attendance for 5 days: 15 hours of training
  • Attendance for 4 days: 12 hours of training
  • Attendance under 4 days: no hours of training will be credited

Catalogue de formation, Doctorat, English speaking courses, Mathématiques et statistiques, Sciences des données

Public

Ph.D students who have completed the « Applied Data Analytics » course

Duration

5 days / 15 hours

Langue

Anglais

Format

Online

Code

DF26ONAA

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