FOUNDATIONS OF TRUSTWORTHY MACHINE LEARNING

The course Foundations of Trustworthy Machine Learning will present an in-depth exploration of trustworthiness of AI/ML from a security and privacy perspective. The course will be research-led, incorporating recent work in the intersection between AI and Cybersecurity.

Learning outcomes of this course are:

  • Understand and apply concepts and algorithms of machine learning to solve cybersecurity specific problems
  • Implement, evaluate, and compare machine learning algorithms that are privacy-preserving and robust to attacks
  • Understand and apply concepts related to the security of AI Models, including attacks and defence methods.

The course consists of asynchronous lessons, divided into 4 units:

  1. Foundations of AI & Cybersecurity
  2. AI-based cybersecurity
  3. Security of AI models
  4. Privacy-preserving AI models

 

  • EUNICE student (Master or PhD): enrolled as master’s degree student or as a PhD student in one of the universities of EUNICE European University consortium (check its members here).
  • B2 level of English.
  • Foundational knowledge of AI/ML from UG studies

Study Level: Master, PhD

  • Submit your application via the button ‘Apply Now’.
  • Please keep in mind that the number of participants could be limited for each course. Application does not guarantee enrolment in the course.
  • The course participants will be selected based on criteria specified in the study guide.
  • Your home university will inform you whether you have been accepted and provide further information about the next steps.

Specific instructions in some universities:

  • BTU students: for questions about enrolment and recognition at your university, you can visit this website.
  • UPHF students: make sure to ask the approval of your director of studies (responsable pédagogique) before applying. For any question, you can contact the EUNICE office: eunice@uphf.fr.
  • UoP students: questions about enrolment and recognition can be answered by your Director of Studies or ECTS Coordinator, or you can contact eunice@go.uop.gr.

Any questions about enrolment or credit recognition? Contact your EUNICE course coordinator.

Apply now

Towards trustworthy AI: safely navigating the AI landscape from a security and privacy perspective

Study Level
Master, PhD
Applications deadline
15 January 2025
Dates
1 February - 30 June, 2025
Accreditation
4 ECTS
Mode
Online self-study