This advanced course provides a deep dive into the fundamental probabilistic and statistical concepts essential for understanding and building Generative AI models. As Generative AI continues to revolutionize fields like NLP, computer vision, and creative content generation, mastering the underlying mathematical principles is crucial for researchers and engineers.

Throughout this course, students will explore:

• Probability Theory in AI – Understanding random variables, probability distributions (Gaussian, Bernoulli, Poisson, etc.), and their role in generative models.
• Bayesian Inference & Uncertainty Estimation – Leveraging Bayesian networks and priors for AI decision-making.
• Markov Chains & Stochastic Processes – Studying how Markov models power text generation and reinforcement learning.
• Information Theory – Entropy, KL divergence, and their applications in generative models like VAEs and diffusion models.
• Statistical Learning Methods – Hypothesis testing, MLE, MAP estimation, and their role in AI optimization.
• Monte Carlo Methods & Sampling Techniques – Importance of Gibbs Sampling, Metropolis-Hastings, and their use in probabilistic AI models.

By the end of this course, participants will gain strong mathematical intuition and practical skills for applying statistical reasoning in building and fine-tuning state-of-the-art Generative AI models like GANs, VAEs, Diffusion Models, and Transformers.

Who Should Take This Course?

• AI/ML Engineers & Data Scientists aiming to improve their understanding of probabilistic AI models.
• Researchers in Deep Learning and Generative AI looking to strengthen their statistical foundation.
• Students or professionals seeking to develop Mathematical AI intuition for advanced model design.