Ethics, bias and limitations of machine learning

This module is the third and last part of the "Data Science and Modeling" certificate. The objective is to explore the main challenges of machine learning and the ethical and legal considerations of data use in the business world.

OBJECTIVES

This course will give you :

• The knowledge necessary to evaluate and analyze the performance of a machine learning model.
• The skills required to deal with small databases and manage overlearning.
• The keys to develop critical thinking skills on the challenges of AI and its social and ethical implications.

CONTENT

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  Day 1: Machine Learning (ML) model assessment

  Handling the main challenges of machine learning, model evaluation and performance analysis.

  The main concepts covered in this module are: ML model evaluation, performance analysis and overlearning vs. underlearning.

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  Day 2: Working with a small database

  Learn the methods for managing small amounts of data and how to increase data.

  The main concepts covered in this module are data augmentation and ensemble methods.

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  Day 3: Challenges in the application of the learning machine

  How to create a strategy around machine learning models? What are the social and ethical implications of such choices?

  The main concepts addressed in this module are: performance analysis, deployment of ML models and the ethical, legal and social issues of data use.

TRAINERS/TEACHERS

Emmanuel R. Goffi

Expertise : AI Ethics

Associate Professor of AI Ethics and Director of International Relations | Director of the Ethics & IA Observatory - Sapiens Institute

See his profile

Virginie Mathivet

Expertise : AI & Deep learning

Expert AI & Deep learning teacher | Director of R&D, Corporate Teamwork

See her profile