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"Probabilistic Programming for Machine Learning"
امیرعباس اسدی
Bayesian Learning provides a natural framework for approaching Machine Learning problems. For a long time, due to the significant computational cost of Bayesian inference, this framework was limited to simple models and problems with a small amount of data. Probabilistic Programming is the fruit of many years of research in approximate Bayesian inference aiming to address these limitations. This presentation is a friendly introduction to Probabilistic Programming. We will explore how modern inference methods and recent advances in Differentiable Programming can help us unlock the full potential of Bayesian Machine Learning.
Presentation outline:
- Bayesian Learning and Probabilistic Programs
- Probabilistic Programming in Julia
- Approximate Bayesian Inference
-- Markov Chain Monte Carlo
-- Variational Inference
- Differentiable Programming
- Discussing some examples:
-- Bayesian Deep Learning
-- Bayesian Neural Differential Equations
-- Inverse Optimization
پیشنیاز های علمی: آمار و احتمال مقدماتی، آشنایی با Deep Learning
