Parameter Estimation Courses and Certifications

INI Seminar Room 2

Sequential Bayesian Methods for Parameter Estimation and Applications with Imaging Data

Discover sequential Bayesian approaches for parameter estimation in dynamical systems, focusing on time-varying parameters and practical applications with imaging data.

• YouTube
• 1 hour 14 minutes
• Self-Paced
• Free Video

JuliaHub

Model Calibration and Parameter Estimation with JuliaSim Model Optimizer

Master parameter estimation and model calibration techniques using JuliaSim Model Optimizer to match data with models through automated optimization, inverse problem solving, and model autocomplete features.

• YouTube
• 51 minutes
• Self-Paced
• Free Video

JuliaHub

Control of Rotary Pendulum - Parameter Estimation Using Maximum-Likelihood - Part 5

Dive into maximum-likelihood estimation for rotary pendulum control using unscented Kalman filters and gradient-based optimization with automatic differentiation in Julia.

• YouTube
• 15 minutes
• Self-Paced
• Free Video

International Centre for Theoretical Sciences

Parameter Estimation in the Presence of Temporal Correlations

Explore parameter estimation techniques for systems with temporal correlations in quantum trajectories, focusing on theoretical challenges and mathematical approaches to quantum measurement and control.

• YouTube
• 44 minutes
• Self-Paced
• Free Video

VinAI

Parameter Estimation and Interpretability in Bayesian Mixture Models

Explore posterior contraction in Bayesian mixture modeling, examining parameter estimation and model interpretability under correct and misspecified conditions. Gain insights into tradeoffs between expressiveness and interpretability.

• YouTube
• 1 hour 30 minutes
• Self-Paced
• Free Video

The Julia Programming Language

PEtab.jl - Efficient Parameter Estimation for Dynamic Models

Explore efficient parameter estimation for dynamic models in biology using PEtab.jl. Learn to import problems, set up estimations, and choose optimal gradient computation methods.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

UofU Data Science

Linear Regression and Parameter Estimation in R - Lecture 17

Master linear regression techniques in R while exploring parameter estimation and random variable estimators through hands-on statistical analysis and practical applications.

• YouTube
• 1 hour 20 minutes
• Self-Paced
• Free Video

UofU Data Science

Hidden Markov Models: Parameter Estimation, Inference, and Viterbi Algorithm

Master parameter estimation and inference in Hidden Markov Models (HMM), including Viterbi algorithm implementation for sequence analysis and probabilistic modeling.

• YouTube
• 34 minutes
• Self-Paced
• Free Video

Building a Successful Photography Business in a Small Market

Building a Successful Photography Business in a Small Market with Clay Cook

• CreativeLive
• 1 hour 27 minutes
• Self-Paced
• Paid Course

Curve Fitting with SciPy

Explore SciPy's curve fitting capabilities to model data effectively, mastering essential functions, algorithms, and techniques for precise data analysis in Python.

• CodeSignal
• 1 hour
• Self-Paced
• Free Certificate

Understand and Use Math Models in Real Life

Explore how to apply expressions, equations, and inequalities to real-life situations by substituting values, estimating results, interpreting constraints, and explaining mathematical models in everyday terms.

• CodeSignal
• 2 hours
• Self-Paced
• Free Certificate

Estimating and Comparing Real Numbers

Master the art of approximating irrational numbers using trial squaring, calculator rounding, and number line plotting to compare and estimate real-world arithmetic expressions.

• CodeSignal
• 1 hour
• Self-Paced
• Free Certificate

statisticsmatt

Parameter Estimation

Explore various parameter estimation methods, their optimality properties, and key statistical concepts like sufficient statistics, Bayesian estimation, and Fisher's information.

• YouTube
• 5 hours
• Self-Paced
• Free Video

Probabilistic Graphical Models 3: Learning

304 ratings at Coursera

Learn to build probabilistic graphical models from data, covering parameter estimation, structure learning, and handling incomplete data. Explore advanced techniques for both directed and undirected models.

• Coursera
• 66 hours
• Self-Paced
• Paid Course

误差理论与测量平差基础

Master error theory and measurement adjustment fundamentals for surveying and mapping, covering optimal estimation, adjustment models, and dynamic measurement systems with practical applications in GNSS, photogrammetry, and engineering.

• XuetangX
• Self-Paced
• Free Online Course