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.
Programme
Program Overview
- The detailed program & Syllabus are accessible HERE
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Monday 11/01/27 |
Tuesday 12/01/27 |
Wednesday 13/01/27 | Thursday 14/01/27 |
Friday 15/01/27 |
|
10h00-11h20 Introduction |
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
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11h30-13h00 Probabilistic Modelling
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11h30-13h00 Hyperparameter Tuning |
11h30-13h00 Knowledge-Informed Neural Networks
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Formateurs et formatrices
Formateur : Yvonne Becherini
Email (contact pédagogique): becherini@u-paris.fr
Informations pratiques
Practical Information
Dates : Starting on the 12th of January 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
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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