Bayesian Machine Learning and Probabilistic Modeling Course

Master Bayesian machine learning, posterior inference, hierarchical models, uncertainty validation and probabilistic decision-making.

Artificial Intelligence and Machine Learning Courses Confirmed

Training Locations

This Bayesian Machine Learning and Probabilistic Modeling 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

Many organisational decisions must be made with limited data, changing conditions and genuine uncertainty. Bayesian machine learning provides a coherent framework for combining prior knowledge with observed evidence and expressing uncertainty through probability distributions. This enables professionals to move beyond single-point predictions and reason directly about risk, credible ranges and decision consequences.

This five-day course develops a rigorous, practical foundation in Bayesian machine learning and probabilistic modelling. Participants will construct probabilistic models, perform posterior inference, apply hierarchical and latent-variable approaches, diagnose computational reliability and connect posterior evidence to decisions. The course is technically substantial while remaining focused on real analytical applications.

Objectives

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

Formulate models using priors, likelihoods and posteriors.
Interpret posterior and predictive uncertainty.
Build Bayesian regression and classification models.
Apply sampling and variational inference methods.
Diagnose convergence and computational reliability.
Design hierarchical and latent-variable models.
Compare models through predictive evidence.
Use Bayesian results in risk-based decisions.

Course Content

Day 1: Bayesian Reasoning and Probability

Uncertainty and probabilistic representation
Bayes’ theorem and conditional probability
Priors, likelihoods and posterior distributions
Conjugate models and analytical updating
Posterior prediction and credible intervals
Bayesian updating laboratory

Day 2: Bayesian Predictive Models

Bayesian linear regression
Bayesian logistic classification
Prior design and regularisation
Posterior predictive distributions
Parameter uncertainty and interpretation
Probabilistic prediction laboratory

Day 3: Computational Inference

Monte Carlo estimation
Metropolis–Hastings and Gibbs sampling
Hamiltonian Monte Carlo and NUTS
Variational inference
Convergence and sampling diagnostics
Posterior-computation laboratory

Day 4: Hierarchical and Probabilistic Models

Hierarchical and multilevel modelling
Partial pooling across groups
Latent-variable and mixture models
Probabilistic graphical models
Gaussian processes and state-space models
Probabilistic-model design review

Day 5: Model Criticism and Decision Analysis

Posterior predictive checking
Calibration and uncertainty validation
Predictive model comparison
Prior and assumption sensitivity
Expected utility and decision thresholds
Capstone Bayesian decision model

Training Schedule

Below is the table of cities along with the respective dates for the upcoming training sessions of Bayesian Machine Learning and Probabilistic Modeling 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.