Description
Bayesian Methods in Machine Learning: Uncertainty-Aware AI for Real-World Decisions
Most ML models give a single answer—but what if you need to know **how confident** the model is? Bayesian methods quantify uncertainty, enabling safer decisions in healthcare, finance, and robotics. This course—part of the **Advanced ML Specialization**—covers MCMC, variational inference, and Gaussian processes with hands-on projects.
What You’ll Build
- A Bayesian neural network that predicts not just output, but prediction intervals
- A variational autoencoder for probabilistic generative modeling
- A Bayesian optimizer for hyperparameter tuning and experimental design
- A topic model using Latent Dirichlet Allocation (LDA)
Key Techniques
- Markov Chain Monte Carlo (MCMC)—Metropolis-Hastings, Gibbs sampling
- Variational Inference—mean-field approximation, reparameterization trick
- Gaussian Processes—non-parametric Bayesian regression
- Bayesian Deep Learning—Bayes by Backprop, dropout as Bayesian approximation
Who Is This For?
- Graduate students in ML or statistics
- Researchers exploring probabilistic modeling
- Engineers building safety-critical AI systems
- Kagglers seeking advanced ensemble techniques
Why Bayesian ML?
In a world of overconfident AI, **uncertainty quantification is the next frontier**—and this course puts you at the forefront.
Ready to build AI that knows what it doesn’t know? Enroll now.
