Lectures related to Covid and the Lockdown Effect

COVID-19 Outbreak: Epidemiology and Mitigation

Explores the COVID-19 outbreak, its terminology, transmission, severity, and global impact, emphasizing the importance of mitigation strategies and digital epidemiology.

Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Logistic Regression: Model Interpretation and Comparison

Explores logistic regression model interpretation, parameter estimation, and model comparison using likelihood ratio tests.

Statistical Inference: Model Selection and Nuisance Parameters

Covers model selection, nuisance parameters, and higher-order inference methods in statistical inference.

Probabilistic Linear Regression

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Detecting QTL Hotspots in Sparse Regression Models

Focuses on large-scale inference for detecting QTL hotspots in sparse regression models, emphasizing the need to use genomics to understand variation in phenotypes and disease susceptibility.

Epidemiological Parameters Inference from Clinical Data

Explores the challenges of inferring epidemiological parameters from clinical data, focusing on COVID-19 and the complexities of estimating infection fatality ratios.

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Eliminating Nuisance Parameters: Lemmas in Statistical Inference

Explores the elimination of nuisance parameters in statistical models using Lemmas 14 and 15.

Latent Variable Models

Explores latent variable models, EM algorithm, and Jensen's inequality in statistical modeling.

Machine Learning in Credit Risk Modeling

Delves into the challenges and opportunities of machine learning in credit risk modeling, comparing traditional statistical models with machine learning methods.

Eliminating Nuisance Parameters: Statistical Inference

Covers the elimination of nuisance parameters in statistical inference using Lemmas 14 and 15.

Statistical Models: Unitary Conformal Field Theories

Covers the connection between statistical models and unitary conformal field theories, focusing on critical points and the role of local fields.

Linear Regression and Least Squares Method

Explores linear regression and the least squares method to minimize the loss function for the best fit.

Maximum Likelihood: Inference and Model Comparison

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Bayesian Estimation: Unsupervised Learning & MCMC

Explores Bayesian estimation for unsupervised learning and MCMC, using a Spin Glass Card game example.

Binary Covariate Impact: 2x2 Contingency Tables

Explores the impact of a binary covariate on binary responses using 2x2 tables.

Estimation and Method of Moments

Covers the definition of statistics and estimators, examples of estimators, and the method of moments.

Parametric Models: Mathematics of Data

Explores parametric models in data analysis, covering regression estimators, optimization problems, and statistical models.

Probability and Statistics: Basics and Applications Log in to Mediaspace to watch this video

Covers fundamental concepts of probability and statistics, focusing on data analysis, graphical representation, and practical applications.