Master's in Mechanical Engineering Structure

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Related lectures (28)

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Sampling Theory: Statistics for Mathematicians

Covers the theory of sampling, focusing on statistics for mathematicians.

Probability and Statistics

Covers inequalities, joint Gaussian distribution, risk estimation, and classification method testing in probability and statistics.

Multivariate Statistics: Wishart and Hotelling T²

Explores the Wishart distribution, properties of Wishart matrices, and the Hotelling T² distribution, including the two-sample Hotelling T² statistic.

Heavy-Tailed Distributions

Explores heavy-tailed distributions, the Hill estimator, convergence to Gaussian, and distribution comparison.

Advanced Probabilities: Random Variables & Expected Values

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Node Degree and Strength

Explores node degree and strength in network neuroscience, discussing random vs real networks and the challenges of fitting power laws to real data.

Sampling Distributions: Theory and Applications

Explores sampling distributions, estimators' properties, and statistical measures for data science applications.

Distributions & Interpolation Spaces

Covers distributions, interpolation spaces, convergence, and the concept of dual spaces.

Handling Network Data

Covers handling network data, types of graphs, centrality measures, and properties of real-world networks.

Estimators and Confidence Intervals

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.