Bayesian AI: Unlocking Uncertainty in Machine Learning Training Course

Introduction

In the world of machine learning, most models provide a single-point prediction, which can often be misleading without an understanding of its confidence. Bayesian methods, by contrast, provide a powerful framework for quantifying and reasoning about uncertainty. By incorporating prior beliefs and updating them with new data, Bayesian models don't just give you an answer—they give you a full distribution of possible answers, empowering you to make more informed, risk-aware decisions.

This five-day training program is designed to guide you through the principles and applications of Bayesian machine learning. You will learn to move beyond deterministic models and embrace a probabilistic approach to problem-solving. Through hands-on exercises and real-world case studies, you will master techniques for building models that are not only accurate but also transparent about their predictions, making them invaluable for critical applications in science, finance, and healthcare.

Duration 5 days

Target Audience This course is for data scientists, machine learning engineers, and researchers who have a solid understanding of statistical concepts and are looking to apply a more rigorous, probabilistic approach to their work.

Objectives

  • To understand the fundamental principles of Bayes' theorem and its application to machine learning.
  • To master the implementation of Bayesian linear and logistic regression.
  • To learn how to use Markov Chain Monte Carlo (MCMC) methods for complex models.
  • To gain practical experience with Bayesian neural networks.
  • To explore the principles of Bayesian optimization for hyperparameter tuning.
  • To understand the difference between Maximum Likelihood Estimation (MLE) and Maximum A Posteriori (MAP).
  • To apply Bayesian methods to real-world problems with small or limited data.
  • To develop a systematic approach to evaluating probabilistic predictions.
  • To understand the role of prior distributions and how to choose them.
  • To explore advanced topics like Variational Inference and its applications.

Course Modules

Module 1: The Foundations of Bayesian Inference

  • A review of probability theory and Bayes' theorem.
  • The concepts of prior, likelihood, and posterior distributions.
  • An intuitive introduction to Bayesian thinking.
  • A discussion on the difference between frequentist and Bayesian approaches.
  • A hands-on exercise with a simple Bayesian coin-toss problem.

Module 2: Bayesian Linear Regression

  • The principles of Bayesian linear regression.
  • The role of priors on the weights and bias.
  • Deriving the posterior distribution.
  • A practical guide to implementing Bayesian linear regression from scratch.
  • A discussion on the benefits of a probabilistic model.

Module 3: Probabilistic Programming

  • The concept of probabilistic programming.
  • An introduction to a probabilistic programming language like PyMC or Stan.
  • A hands-on exercise to build a probabilistic model.
  • A discussion on the advantages of using a dedicated framework.
  • A review of the core components of a probabilistic program.

Module 4: Markov Chain Monte Carlo (MCMC)

  • The need for approximate inference in complex models.
  • The core concepts of MCMC methods.
  • An intuitive walkthrough of the Metropolis-Hastings algorithm.
  • A hands-on exercise to implement MCMC for a simple model.
  • A discussion on the convergence and diagnostics of MCMC chains.

Module 5: Bayesian Logistic Regression

  • The principles of Bayesian logistic regression.
  • Applying Bayesian methods to classification problems.
  • The use of MCMC to infer the posterior distribution.
  • A practical guide to implementing Bayesian logistic regression.
  • A discussion on how to interpret probabilistic predictions.

Module 6: Hierarchical Bayesian Models

  • The concept of hierarchical models.
  • Applying hierarchical models to grouped or multi-level data.
  • The benefits of "borrowing strength" across groups.
  • A hands-on exercise with a hierarchical model.
  • A discussion on the practical applications of these models.

Module 7: Bayesian Neural Networks (BNNs)

  • The concept of placing priors over the weights of a neural network.
  • The challenges of training BNNs.
  • The use of Variational Inference as an alternative to MCMC.
  • A practical guide to implementing a BNN.
  • A discussion on the uncertainty quantification provided by BNNs.

Module 8: Model Comparison and Evaluation

  • The challenges of model comparison in a Bayesian context.
  • The use of Posterior Predictive Checks.
  • The role of Bayesian Information Criterion (BIC) and Deviance Information Criterion (DIC).
  • A hands-on exercise with model comparison.
  • A discussion on how to choose the "best" model.

Module 9: Bayesian Optimization

  • The principles of Bayesian optimization for hyperparameter tuning.
  • The role of the surrogate model (Gaussian Process).
  • The concept of the acquisition function.
  • A practical guide to using Optuna or Hyperopt.
  • A discussion on the efficiency benefits over grid and random search.

Module 10: Case Studies

  • A hands-on project to apply Bayesian methods to a real-world dataset.
  • Case studies in finance, healthcare, or scientific research.
  • The project will emphasize proper data preparation and validation.
  • A discussion on how to interpret and communicate probabilistic results.
  • A review of the best practices for building a Bayesian pipeline.

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport Pick Up is provided by the institute. Accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

For More Details call: +254-114-087-180

 

 

Bayesian Ai: Unlocking Uncertainty In Machine Learning Training Course in Namibia
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