🧩 Part 1/8

✔ What is Bayesian inference and why is it important?
✔ How to apply Bayes' theorem to update your beliefs
✔ The components of Bayes' theorem: prior, likelihood, and posterior
✔ The Bayes' rule formula and how to use it
✔ Examples of Bayesian inference in deep learning
✔ Bayesian neural networks and how they differ from standard neural networks
✔ Variational inference and how it approximates the posterior distribution
✔ Challenges and limitations of Bayesian inference in deep learning
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🧩 Part 2/8

✔ What is Uncertainty and Why is it Important?
✔ Types of Uncertainty: Aleatoric and Epistemic
✔ Challenges of Modeling Uncertainty in Deep Learning
✔ What are Bayesian Neural Networks?
✔ Bayesian Inference and Bayes' Theorem
✔ Prior, Posterior, and Likelihood Distributions
✔ Advantages and Limitations of Bayesian Neural Networks
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🧩 Part 3/8

✔ What are Gaussian processes?
✔ Definition and properties
✔ Prior and posterior distributions
✔ How to choose kernel functions?
✔ Common kernels and their characteristics
✔ Hyperparameter optimization and model selection
✔ How to perform Gaussian process regression?
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🧩 Part 4/8

✔ Bayesian Inference and Variational Inference
✔ Bayesian Inference
✔ Variational Inference
✔ Variational Lower Bound and Kullback-Leibler Divergence
✔ Variational Lower Bound
✔ Kullback-Leibler Divergence
✔ Mean-Field Approximation and Coordinate Ascent
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🧩 Part 5/8

✔ What are Variational Autoencoders?
✔ The Encoder-Decoder Architecture
✔ The Latent Variable Model
✔ The Reparameterization Trick
✔ How to Train Variational Autoencoders?
✔ The Evidence Lower Bound
✔ The Kullback-Leibler Divergence
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🧩 Part 6/8

✔ Probabilistic Modeling and Density Estimation
✔ Normalizing Flows: Definition and Properties
✔ Change of Variables Formula and Invertible Transformations
✔ Types of Normalizing Flows and Examples
✔ Applications of Normalizing Flows in Deep Learning
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🧩 Part 7/8

✔ What are Generative Adversarial Networks?
✔ The Generator
✔ The Discriminator
✔ The Adversarial Loss
✔ Applications of Generative Adversarial Networks
✔ Image Generation
✔ Data Augmentation
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🧩 Part 8/8

✔ Reinforcement Learning Basics
✔ Bayesian Methods for Reinforcement Learning
✔ Bayesian Exploration-Exploitation Trade-off
✔ Bayesian Optimization for Hyperparameter Tuning
✔ Thompson Sampling for Bandit Problems
✔ Applications and Examples
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