Control Bootcamp
Rapid overview of optimal control concepts, covering controllability, observability, LQR, and Kalman filter. Applies main approaches to simple Matlab examples for practical understanding.
Kalman Filter for Beginners - Recursive Filters & MATLAB Examples
Explore recursive filters, including average, moving average, and low-pass filters, with practical MATLAB examples. Learn the foundations of Kalman filtering for enhanced estimation and data analysis skills.
Control Bootcamp - Kalman Filter Example in Matlab
Explore Kalman Filter implementation on inverted pendulum using Matlab, covering theory and practical application in dynamical systems and control engineering.
Virtual Reality Engineering
Explore psychophysics, visual perception, haptics, and challenges in VR/AR. Gain insights into human sensory systems and cutting-edge interfaces for immersive experiences.
Estimation of Signals and Systems
This course covers lessons on probability theory, random variables, mean and variance, linear signal models, z-transform, Kalman filter, variants of least squares estimation, and estimation problems in instrumentation and control.
Optimal Control, Guidance and Estimation
Comprehensive exploration of optimal control theory, guidance systems, and estimation techniques for aerospace applications, covering LQR, dynamic programming, and Kalman filters.
Real-Time Airborne Magnetic Navigation with MagNav.jl
Discover how MagNav.jl enables real-time magnetic navigation by compensating aircraft magnetic fields and implementing advanced navigation filters using Julia's powerful features.
Kalman Filter for Beginners - Estimation and Prediction Process & MATLAB Example
Explore Kalman Filter estimation and prediction processes, system modeling, and MATLAB examples. Learn practical applications for dynamic attitude estimation in aerospace engineering.
State Estimation of Complex Systems
Explore state estimation techniques for complex systems, focusing on advanced methods and applications in dynamical systems and machine learning.
Data Assimilation - Kalman Filter - Lecture 7
Explore Kalman Filter in data assimilation for weather and climate models. Learn theoretical foundations and practical applications of this crucial mathematical approach.
The Legacy of Rudolph Kalman - Systematic Approaches to Data-Driven Predictive Modeling
Explore Rudolph Kalman's groundbreaking work on data-driven mathematical modeling and its impact on fields like space control, weather prediction, and AI. Gain insights into optimization and iteration concepts.
Time Series Class - Part 1 - Dr Ioannis Papastathopoulos, University of Edinburgh
Learn fundamental concepts and advanced techniques for analyzing time series data, including ARMA models, state-space models, and neural networks for forecasting and inference.
Time Series Class - Part 1 - Dr. Ioannis Papastathopoulos, University of Edinburgh
Learn fundamental concepts and advanced techniques in time series analysis, including ARMA models, state-space models, and neural networks for forecasting and inference.
Time Series Class - Part 2 - Professor Chris Williams, University of Edinburgh
Explore time series analysis, from moving average models to advanced state-space techniques and recurrent neural networks. Gain practical skills in parameter estimation, forecasting, and applying models to real-world data.
An Introduction to State-Space Models, Particle Filters, and Sequential Monte Carlo - Lecture 1
Explore state-space models, particle filters, and sequential methods in Bayesian statistics with Nicolas Chopin's comprehensive introduction to these advanced mathematical concepts.
Fast Probabilistic Inference for ODEs with ProbNumDiffEq.jl
Explore probabilistic ODE solvers with ProbNumDiffEq.jl. Learn Bayesian inference for numerical problems, implementation details, and future prospects in the Julia ecosystem.
Large Scale High Temporal Resolution Effective Connectivity Analysis
Explore effective connectivity analysis in MEG, addressing challenges in neural data interpretation and introducing advanced techniques for high-resolution brain network mapping.
Computational Principles of Sensorimotor Control - Lecture 1
Explore computational principles of sensorimotor control, including optimal feedback control, state estimation, motor learning, and Bayesian decision theory in human movement and robotics.
Numerical Weather Prediction Methods
Explore numerical weather prediction methods, including data assimilation techniques, uncertainty estimation, and applications in atmospheric and climate modeling.
Satellite Data Assimilation - Techniques and Applications
Explore satellite data assimilation techniques for weather and climate modeling, focusing on theory, methods, and practical applications in atmospheric and oceanic sciences.
Data Assimilation and Dynamical Systems in Earth Sciences - Lecture 3
Explore data assimilation theory and applications in Earth sciences, focusing on dynamical systems. Gain hands-on experience with practical techniques for integrating observations and forecasts in weather and climate models.