Lectures for Statistical Theory and Modelling, 7.5 hp

This is just a skeleton template for a course that will be given in the future.

Lecture 0 - (really) Basic maths
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Lecture 1 - Introduction. Discrete random variables.
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Lecture 2 - Continuous random variables. Probability density functions. Integration.
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Lecture 3 - Joint and conditional distributions. Covariance and correlation. Bayes theorem.
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Lecture 4 - Functions. Derivatives. Mathematical optimization.
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Lecture 5 - Transformation of random variables. Monte Carlo simulation. Law of large numbers. Central limit theorem.
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Lecture 6 - Point estimation. Maximum likelihood.
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Lecture 7 - Vectors and matrices. Multivariate normal distribution.
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Lecture 8 - Linear regression in vector form.
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Lecture 9 - Sampling distributions. Observed and Fisher information. Numerical optimization.
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Lecture 10 - Logistic regression.
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Lecture 11 - Nonlinear regression. Interactions. Overfitting. Regularization. Cross-validation. Bias-variance trade-off
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Lecture 12 - Time series. Autocorrelation function. Autoregressive models.
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Lecture 13 - Course summary and example exam.
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