Computational Systems Biology
Explore computational approaches to model and analyze biological systems, from network biology to dynamic modeling, with hands-on labs using MATLAB and other tools.
Pattern Recognition
Comprehensive exploration of statistical pattern recognition, covering Bayes classifiers, density estimation, linear discriminants, neural networks, SVMs, and advanced learning techniques.
Linear Regression Analysis and Forecasting
The accuracy of forecasting depends upon the goodness of obtained model. What are the steps and checks required to obtain a good model and in turn, how to do forecasting is being aimed to be taught in this course.
Signals and Systems II
Comprehensive exploration of advanced signal processing concepts, including probability theory, random variables, stochastic processes, and spectral analysis for electrical engineering applications.
Parameter Estimation in Mathematical Oncology - Lecture
Dive into advanced techniques for parameter estimation in mathematical oncology, exploring key concepts and practical applications.
Using R
Learn advanced statistical techniques and data analysis using R, covering distributions, simulations, estimations, and various statistical tests through practical examples and hands-on coding.
Maximum Likelihood Estimation
Comprehensive exploration of Maximum Likelihood Estimation across various probability distributions, including practical applications using R for data generation and parameter estimation.
Parameter Estimation
Explore various parameter estimation methods, their optimality properties, and key statistical concepts like sufficient statistics, Bayesian estimation, and Fisher's information.
Method of Moments Topics
Explore estimation techniques for various probability distributions using the Method of Moments, including gamma, uniform, normal, binomial, and Pareto.
La métrologie quantique et ses applications
Découvrez comment l'intrication quantique améliore la précision des mesures en métrologie, avec des applications pour le temps, le magnétisme et la gravité.
Inferring Parameters of Random Graph Models from Induced Stochastic Dynamics
Explore advanced concepts in discrete probability and statistics through talks, minicourses, and collaborative sessions. Enhance research skills in random graph models and network dynamics.
Regresión Bridge Ajustada Mediante SURE
Explora la regresión Bridge ajustada con SURE, una técnica avanzada de análisis estadístico para mejorar la precisión en modelos predictivos.
Comparación de Estimadores Robustos para los Parámetros de una Distribución Beta - Parte 2
Explora estimadores robustos para parámetros de distribución Beta P2 con Roberto C. Duarte en una charla concisa de 18 minutos.
3D Atomic Resolution Through Dose-Efficient Fusion of Image & Analytical Technique
Explore 3D atomic structure analysis of nanomaterials using advanced quantitative TEM techniques, including parameter estimation, deep learning, and fusion of ADF STEM with EDX for heterogeneous materials.
Discovering Dynamics and Parameters of Nonlinear Systems from Partial Observations
Explore techniques for uncovering nonlinear system dynamics and parameters using incomplete data, enhancing understanding of complex systems and their behavior.
What Mathematical Models Can and Cannot Tell Us About the Spread of Infectious Diseases - Lecture 2
Explore mathematical modeling in disease spread: capabilities, limitations, and insights for understanding epidemic dynamics and informing public health strategies.
Nuts and Bolts of Modern State Space Models - Part I
Explore state space models for analyzing sequential data, covering foundations, algorithms, and applications using the dynamax library. Learn to uncover latent states in high-dimensional measurements.
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.
Simulation - The Challenge for Data Science
Exploring the fusion of simulation models and machine learning for policy analysis, addressing challenges in parameter estimation and initialization for complex systems forecasting.