Adversarial Machine Learning and Model Robustness Course

Learn adversarial ML, threat modelling, attack testing, robust defences, privacy controls and secure MLOps for resilient production models.

Artificial Intelligence and Machine Learning Courses Confirmed

Adversarial Machine Learning and Model Robustness Course

Training Outlines

Introduction

Machine-learning systems create attack surfaces across data, models, interfaces and supply chains. Adversaries may manipulate inputs, poison training data, implant backdoors or extract sensitive information, while ordinary distribution shifts can expose similar weaknesses. Robustness therefore requires structured threat modelling and evidence, not isolated defensive techniques.

This five-day course gives machine-learning and security professionals a practical framework for adversarial testing and resilient deployment. Participants will examine evasion, poisoning, backdoor, extraction and privacy attacks; compare defence strategies; measure robustness; and integrate security controls into the ML lifecycle. Laboratories build toward a defensible production architecture and response plan.

Objectives

By the end of this course, participants will be able to:

Construct risk-based ML threat models.
Generate and test adversarial examples.
Recognise poisoning and backdoor attacks.
Assess extraction, inversion and privacy risks.
Apply robust training and detection controls.
Evaluate shift, uncertainty and calibration.
Integrate security monitoring and incident response.
Design a robust ML architecture.

Course Content

Day 1: Threat Modelling for Machine Learning

ML attack surfaces and trust boundaries
Assets, actors and attack objectives
White-box and black-box access
Evasion, poisoning and extraction taxonomy
Robustness, safety and security distinctions
ML threat-modelling workshop

Day 2: Evasion and Adversarial Examples

Gradients, perturbations and decision boundaries
FGSM and iterative attacks
Transferability and black-box attacks
Physical-world adversarial attacks
Attack success and robustness metrics
Adversarial-testing laboratory

Day 3: Data, Model and Privacy Attacks

Training-data poisoning
Backdoors and trigger patterns
Pretrained-model supply-chain risks
Model extraction and cloning
Inversion and membership inference
Controlled attack-simulation laboratory

Day 4: Defensive Engineering

Adversarial training
Input validation and attack detection
Robust objectives and certification concepts
Differential privacy and access controls
Out-of-distribution detection and calibration
Defence-comparison laboratory

Day 5: Secure MLOps and Response

Secure data and model lineage
Red-team testing and release gates
Attack and drift monitoring
Incident response and rollback
Risk documentation and change control
Capstone robust-architecture design

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

Jakarta

Indonesia

Cork

Ireland

Dublin

Ireland

Florence

Italy

Milan

Italy

Naples

Italy

Rome

Italy

Osaka

Japan

Tokyo

Japan

Pristina

Kosovo

Prizren

Kosovo

Liepāja

Latvia

Riga

Latvia

Schaan

Liechtenstein

Vaduz

Liechtenstein

Kaunas

Lithuania

Vilnius

Lithuania

Esch-sur-Alzette

Luxembourg

Luxembourg City

Luxembourg

Kuala Lumpur

Malaysia

Malé

Maldives

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

Abuja

Nigeria

Lagos

Nigeria

Port Harcourt

Nigeria

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

Singapore

Bratislava

Slovakia

Košice

Slovakia

Bled

Slovenia

Ljubljana

Slovenia

Incheon

South Korea

Seoul

South Korea

Barcelona

Spain

Madrid

Spain

Valencia

Spain

Sri Jayawardenepura Kotte

Sri Lanka

Gothenburg

Sweden

Stockholm

Sweden

Bern

Switzerland

Geneva

Switzerland

Zurich

Switzerland

Kaohsiung

Taiwan

Taipei

Taiwan

Ankara

Turkey

Istanbul

Turkey

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 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.