Hidden Markov Models (HMM): Theory

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Named Entity Recognition: Applications and Techniques

Explores Named Entity Recognition, its uses, techniques, and applications in information extraction.

Time Series Clustering

Covers clustering time series data using dynamic time warping, string metrics, and Markov models.

Neurobiological Signals: Processing and Classification

Explores neurobiological signal processing, including spike modeling, de-noising, and data classification techniques.

Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Parametric Signal Models: Markov Chains

Explores parametric signal models, including AR processes and Markov chains, covering synthesis, analysis, and correlation structures.

Panel data: dynamic model with panel effects

Covers the Dynamic Markov model with panel effects, addressing initial condition and endogeneity.

HMM - Applications to Robotics

Explores the Bayesian extension of HMM for robot action segmentation and modeling, limitations of classical HMMs, and motion capture data segmentation.

Markov Chains: Ergodicity and Stationary Distribution

Explores ergodicity and stationary distribution in Markov chains, emphasizing convergence properties and unique distributions.

Debiased Whittle Likelihood: Time Series and Spatial Data

Explores the Debiased Whittle likelihood for time series and spatial data, focusing on fitting spectral density to the periodogram for better predictions and parameter estimation.

Lindblad equation

Covers the interpretation of the Lindblad equation and its unitary part in quantum gases.