📌 Probability & Machine Learning: We expect basic knowledge of probability theory and machine learning. You can review Chapters 1–4 of the PML book for a solid foundation.
Murphy, Kevin P. Probabilistic Machine Learning: An Introduction. MIT Press, 2022. A free online version is available here: 🔗 PML Book
📌 Python & Numpy: Some familiarity with Python is also expected. You can use following two Google Colab notebooks to help you get comfortable with Python and NumPy.
- Slides
- Notebook: Bayesian_Network_Example
- Notebook: students_PPLs_Intro
- Notebook: solutions_PPLs_Intro
- Slides
- Notebook: students_simple_model
- Notebook: solutions_simple_model
- Notebook: CAVI-linreg
- Slides
- Notebook: BayesianNeuralNetworks
- Notebook: students_BBVI
- Notebook: solutions_BBVI
- Notebook: student_simple_gaussian_model_pyro
- Notebook: solution_simple_gaussian_model_pyro
- Notebook: Bayesian_linear_regression
- Notebook: students_bayesian_logistic_regression
- Notebook: solutions_bayesian_logistic_regression
- Notebook: students_VAE
- Notebook: solutions_VAE