Lecturer

Marco Lorenzi,

Content and organization

  • Critically assess the performance of the model on a specified task through cross validation and the evaluation of information criteria
  • Identify and prevent the sources of assessment bias
  • Create your own benchmark for a variety of modeling problem
  • Identify modeling alternatives and evaluation strategies
  • Visualize and present performances across models
  • Understand the basis of theoretical approaches to model selection

Course Type

AI PhD Curriculum

Host Institution
Université Côte d'Azur

Other short courses

11. 03. 2025 Go

Deep Learning

13. 02. 2025 Go

Ethics and AI

13. 02. 2025 Go

Computer Vision

19. 01. 2025 Go

Ethics & STICs

10. 04. 2024 Go

Ethics & STICs

01. 03. 2024 Go

Computer Vision

24. 11. 2023 Go

Human Rights Toolbox

21. 02. 2023 Go

Computer Vision

11. 05. 2022 Go

Geometric learning

05. 04. 2022 Go

Computer Graphics

04. 04. 2022 Go

Bayesian Learning

02. 04. 2022 Go

Computer Graphics

31. 03. 2022 Go

Web of Data

28. 03. 2022 Go

Machine Learning

27. 03. 2022 Go

Machine Learning

02. 03. 2022 Go

Player Modeling

28. 02. 2022 Go

Player Modeling

21. 02. 2022 Go

Affective Computing

21. 02. 2022 Go

Machine Listening

21. 02. 2022 Go

Computer Vision

21. 02. 2022 Go

Computer Vision

21. 02. 2022 Go

Self-Driving Cars

21. 02. 2022 Go

Deep Learning

21. 02. 2022 Go

Deep Learning 2

09. 07. 2021 Go

Self-Driving Cars

09. 07. 2021 Go

Computer Vision

09. 07. 2021 Go

Deep Learning

17. 06. 2021 Go

Deep Learning School

17. 06. 2021 Go

Memory Network

02. 06. 2021 Go

Machine Listening

02. 06. 2021 Go