Explainable AI and Model Interpretability Techniques Course

Master explainable AI, model interpretation, SHAP, counterfactuals and deep-model analysis with rigorous validation and governance.

Artificial Intelligence and Machine Learning Courses Confirmed

Training Locations

This Explainable AI and Model Interpretability Techniques 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

Singapore

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

Model performance alone is not enough when artificial intelligence influences consequential, regulated or high-value decisions. Organisations must understand what a model uses, how its behaviour changes and whether an explanation is sufficiently faithful for developers, decision-makers, customers, auditors or regulators. Poor explanations can create false confidence, especially when association is incorrectly presented as causation.

This five-day course develops professional competence in explainable AI and model interpretability. Participants will apply global, local, attribution, counterfactual and deep-model techniques; test their stability and fidelity; and learn how to communicate model behaviour without overstating the evidence. The course is practical, technically rigorous and directly connected to validation and governance requirements.

Objectives

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

Define the purpose and audience of an explanation.
Distinguish transparency, interpretability and causal explanation.
Analyse global model behaviour and feature effects.
Produce and assess local prediction explanations.
Apply attribution and counterfactual techniques responsibly.
Test explanations for fidelity, stability and usefulness.
Communicate limitations to different stakeholder groups.
Integrate interpretability into model validation and governance.

Course Content

Day 1: Explainability Principles and Requirements

Interpretability, explainability and transparency
Global versus local explanations
Intrinsic versus post-hoc interpretation
Stakeholder and decision requirements
Explanation limits and causal overstatement
Interpretability planning workshop

Day 2: Interpretable Models and Global Analysis

Linear and logistic model interpretation
Decision trees and transparent rule systems
Generalised additive and constrained models
Permutation-based feature importance
Partial dependence, ICE and ALE
Global model-behaviour laboratory

Day 3: Local Attribution and Counterfactuals

Local surrogate explanation methods
LIME assumptions and stability
Shapley values and SHAP explanations
Correlated-feature attribution risks
Counterfactual explanations and actionable recourse
Local explanation comparison laboratory

Day 4: Deep-Model Interpretation and Testing

Gradient and saliency methods
Integrated gradients and reference selection
Activation maps for visual models
Attention and concept-based interpretation
Faithfulness, robustness and sanity checks
Deep-model explanation audit

Day 5: Governance and Explanation Communication

Risk-based explanation standards
Model cards and validation evidence
Audience-specific explanation design
Human oversight and contestability
Explanation drift and independent review
Capstone interpretability assessment

Training Schedule

Below is the table of cities along with the respective dates for the upcoming training sessions of Explainable AI and Model Interpretability Techniques 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.