Adversarial Machine Learning and Model Robustness Course

Explore techniques to enhance AI model resilience against adversarial attacks, ensuring robustness and security in machine learning applications.

Artificial Intelligence and Machine Learning Courses Confirmed

Training Locations

This Adversarial Machine Learning and Model Robustness Course is available in multiple cities. Please select your preferred location from the list below

Durrës

Albania

Tirana

Albania

Andorra la Vella

Andorra

Escaldes-Engordany

Andorra

Innsbruck

Austria

Salzburg

Austria

Vienna

Austria

Gomel

Belarus

Minsk

Belarus

Antwerp

Belgium

Brussels

Belgium

Banja Luka

Bosnia and Herzegovina

Sarajevo

Bosnia and Herzegovina

Plovdiv

Bulgaria

Sofia

Bulgaria

Dubrovnik

Croatia

Split

Croatia

Zagreb

Croatia

Limassol

Cyprus

Nicosia

Cyprus

Brno

Czech Republic

Prague

Czech Republic

Aarhus

Denmark

Copenhagen

Denmark

Tallinn

Estonia

Tartu

Estonia

Helsinki

Finland

Tampere

Finland

Lyon

France

Marseille

France

Nice

France

Paris

France

Berlin

Germany

Frankfurt

Germany

Hamburg

Germany

Munich

Germany

Athens

Greece

Thessaloniki

Greece

Budapest

Hungary

Debrecen

Hungary

Akureyri

Iceland

Reykjavík

Iceland

Cork

Ireland

Dublin

Ireland

Florence

Italy

Milan

Italy

Naples

Italy

Rome

Italy

Pristina

Kosovo

Prizren

Kosovo

Liepāja

Latvia

Riga

Latvia

Schaan

Liechtenstein

Vaduz

Liechtenstein

Kaunas

Lithuania

Vilnius

Lithuania

Esch-sur-Alzette

Luxembourg

Luxembourg City

Luxembourg

St. Julian's

Malta

Valletta

Malta

Bălți

Moldova

Chișinău

Moldova

La Condamine

Monaco

Monte Carlo

Monaco

Budva

Montenegro

Podgorica

Montenegro

Amsterdam

Netherlands

Rotterdam

Netherlands

The Hague

Netherlands

Ohrid

North Macedonia

Skopje

North Macedonia

Bergen

Norway

Oslo

Norway

Gdańsk

Poland

Kraków

Poland

Warsaw

Poland

Faro

Portugal

Lisbon

Portugal

Porto

Portugal

Bucharest

Romania

Cluj-Napoca

Romania

City of San Marino

San Marino

Serravalle

San Marino

Belgrade

Serbia

Novi Sad

Serbia

Bratislava

Slovakia

Košice

Slovakia

Bled

Slovenia

Ljubljana

Slovenia

Barcelona

Spain

Madrid

Spain

Valencia

Spain

Gothenburg

Sweden

Stockholm

Sweden

Bern

Switzerland

Geneva

Switzerland

Zurich

Switzerland

Kyiv

Ukraine

Lviv

Ukraine

Odesa

Ukraine

Dubai

United Arab Emirates

Birmingham

United Kingdom

Edinburgh

United Kingdom

London

United Kingdom

Manchester

United Kingdom

Rome (Vatican-adjacent)

Vatican City

Training Outlines

Introduction

This course, "Adversarial Machine Learning and Model Robustness," is designed to provide professionals with a comprehensive understanding of the vulnerabilities in machine learning models and the strategies to safeguard them against adversarial attacks. Throughout this 5-day course, participants will explore the theoretical foundations, practical tools, and real-world applications of adversarial machine learning, equipping them with the necessary skills to enhance model robustness and ensure the integrity and reliability of AI systems in diverse sectors.

Objectives

Understand the key concepts and types of adversarial attacks on machine learning models.
Gain knowledge of the current state-of-the-art defense mechanisms and model robustness techniques.
Learn to implement practical solutions to mitigate adversarial risks in real-world applications.
Explore the ethical implications and security considerations in deploying machine learning models.
Develop the ability to conduct adversarial testing and strengthening of machine learning systems.

Course Outlines

Day 1: Introduction to Adversarial Machine Learning

Overview of machine learning security
Types of adversarial attacks (white box, black box, grey box)
History and evolution of adversarial machine learning
Case studies on high-profile adversarial attacks
Introduction to adversarial threat models

Day 2: Adversarial Attack Techniques

Mathematical foundations of adversarial examples
Gradient-based attack methods (e.g., FGSM, PGD)
Optimization-based attack strategies
Physical world adversarial examples
Hands-on exercises with Python and popular libraries

Day 3: Defense Strategies and Model Robustness

Adversarial training and data augmentation techniques
Gradient masking and its challenges
Use of robust architectures (defensive distillation, input preprocessing)
Detection of adversarial attacks
Evaluating robustness: metrics and benchmarks

Day 4: Practical Applications and Case Studies

Adversarial robustness in computer vision
Threats and defenses in natural language processing
Robustness in reinforcement learning applications
Industrial applications and best practices
Interactive session: Applying concepts to a real-world scenario

Day 5: Ethical and Security Considerations

Ethical implications of adversarial AI
Security considerations and risk management
Regulatory and compliance frameworks
Future trends in adversarial machine learning
Group project presentations and feedback

Training Schedule

Below is the table of cities along with the respective dates for the upcoming training sessions of Adversarial Machine Learning and Model Robustness Course. Please review the schedule to find the most convenient option for you. You can also use the below search bar to type the city name and filter the results.