Probabilistic Machine Learning with Julia Course

Syllabus

• Intoroduction to Julia
• Introduction to Probabilistic Programming
• Probabilistic Modeling
  • Parametric Probabilistic Models
    • Basic Latent Variable Models [Turing.jl]
    • Bayesian Deep Learning [Turing.jl, Lux.jl]
    • Bayesian Differential Equations [DifferentialEquations.jl]
  • Nonparametric Probabilistic Models [GaussianProcesses.jl]
• Inference
  • Markov Chain Monte Carlo [AdvancedMH.jl, AdvancedHMC.jl]
  • Parametric Variational Inference [AdvancedVI.jl]
  • Nonparametric Variational Inference [NonparametricVI.jl]
  • Reactive Message Passing [RxInfer.jl]
• Applications
  • Probabilistic Time Series Modeling
  • Bayesian Optimization
  • Bayesian Reinforcement Learning
  • Active Inference

Lecturer: Amirabbas Asadi, MSc student in Applied Mathematics

Prerequisite: Basics of Probability and Machine Learning, Julia programming

Organization: Dr. Seyed-Mohammad-Mahdi Kazemi, Faculty of Finance, Kharazmi University

Location: Kharazmi University, Faculty of Finance, Class 413

Slides: GitHub

Video Lectures [In Persian]