Gaussian process

Applied sciences
Information engineering
Machine learning
Artificial neural networks

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Related lectures (31)

Conditional Gaussian Generation

Explores the generation of multivariate Gaussian distributions and the challenges of factorizing covariance matrices.

Neural Networks and Quantum Field Theory

Explores the connection between neural networks and quantum field theory, focusing on the correspondence between parameter and function spaces.

Topic Models: Understanding Latent Structures

Explores topic models, Gaussian mixture models, Latent Dirichlet Allocation, and variational inference in understanding latent structures within data.

IT Strategy: Best Practice Processes

Explores best practice IT processes, including ITIL 4 and COBIT 2019 frameworks.

State Space Models: Expressivity of Transformers

Covers state space models and the expressivity of transformers in sequence copying tasks.

Central Limit Theorem: Multivariate Delta Method

Explores the Central Limit Theorem, Slutsky's Theorem, and the Multivariate Delta Method in probability and distribution convergence.

Data Classification: Mixture Models

Explores EMG signal analysis, mixture models, Gaussian models, and spike sorting in neural signal processing using PCA.

Stationary Mean-Field: Neuronal Dynamics

Explores the stationary mean-field concept in computational neuroscience to predict neuronal activity based on population and single neuron firing rates.

Time Series: Fundamentals and Models

Covers the fundamentals of time series analysis, including models, stationarity, and practical aspects.

Equivariant Structural Representations

Explores equivariant structural representations in atomistic machine learning, emphasizing the importance of representing target properties in the spherical basis.

General Linear Processes: Understanding Stationarity and Invertibility

Explores general linear processes, emphasizing stationarity and invertibility in AR, MA, and ARMA models.

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