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📐Linear Algebra15📈Calculus & Differentiation10🎯Optimization14🎲Probability Theory12📊Statistics for ML9📡Information Theory10🔺Convex Optimization7🔢Numerical Methods6🕸Graph Theory for Deep Learning6🔵Topology for ML5🌐Differential Geometry6∞Measure Theory & Functional Analysis6🎰Random Matrix Theory5🌊Fourier Analysis & Signal Processing9🎰Sampling & Monte Carlo Methods10🧠Deep Learning Theory12🛡️Regularization Theory11👁️Attention & Transformer Theory10🎨Generative Model Theory11🔮Representation Learning10🎮Reinforcement Learning Mathematics9🔄Variational Methods8📉Loss Functions & Objectives10⏱️Sequence & Temporal Models8💎Geometric Deep Learning8

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⚙️AlgorithmIntermediate

Expectation Maximization (EM)

Expectation Maximization (EM) is an iterative algorithm to estimate parameters when some variables are hidden or unobserved.

#expectation maximization#em algorithm#e-step+12
📚TheoryIntermediate

Mean Field Variational Family

Mean field variational family assumes the joint posterior over latent variables factorizes into independent pieces q(z) = ∏ q_i(z_i).

#mean field
Advanced
Group:
Variational Methods
#variational inference
#elbo
+11
📚TheoryIntermediate

Variational Inference

Variational Inference (VI) turns Bayesian inference into an optimization problem by choosing a simple family q(z) to approximate an intractable posterior p(z|x).

#variational inference#elbo#kl divergence+12