Lecture

Spatial Dependence: Concepts and Analysis

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Introduces spatial dependence, the paradox of classical statistics, and the bias in linear regression.

Covers fundamental concepts in probability and statistics, emphasizing data analysis techniques and statistical modeling.

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.

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

Covers fundamental concepts in probabilities and statistics, including linear regression, exploratory statistics, and the analysis of probabilities.

Explores linear regression analysis of ozone data using statistical models.

Covers supervised learning with a focus on linear regression, including topics like digit classification, spam detection, and wind speed prediction.

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.

Covers the theory and practical applications of linear regression.

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

Covers linear regression topics including confidence intervals, variance, and maximum likelihood approach.

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Explores the assumptions and checking methods for Gaussian linear regression models using residuals and graphical plots.

Explores linear regression fundamentals, model training, evaluation, and performance metrics, emphasizing the importance of R², MSE, and MAE.

Explores linear regression, least squares, residuals, and confidence intervals in regression models.

Introduces data science topics, evaluation methods, and course organization for the semester.

Covers the basics of linear regression, instrumental variables, heteroskedasticity, autocorrelation, and Maximum Likelihood Estimation.