Lectures related to Robust statistics

Discusses the allocation of inputs and outputs in life cycle inventory calculation.

Explores data representations, histograms, neural networks, and deep learning concepts.

Covers advanced functions of Pandas, focusing on filtering, labeling, and manipulating dataframes.

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

Explores the best approach for imputation in life cycle inventory calculation, covering methods and examples in wood and mineral production.

Explores link prediction, logistic regression, causal inference, and biclustering in statistical network analysis.

Covers techniques for handling missing data and standardizing features, as well as transforming input and output data.