Lectures related to Outlier | EPFL Graph Search

Numerical Methods: Euler and Crank-Nicolson

Covers Euler and Crank-Nicolson methods for solving differential equations.

Probability Distributions in Environmental Studies

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.

Power Method

Covers the Power Method for approximating the largest eigenvalue of a matrix.

Inverse Power Method: Introduction to ODEs

Explores the inverse power method for ODEs and the significance of Lipschitz continuity.

Regression Diagnostics

Covers regression diagnostics for linear models, emphasizing the importance of checking assumptions and identifying outliers and influential observations.

Data Analysis Techniques: Amplitude Shift Keying and Graphical Methods

Covers amplitude shift keying and various data analysis techniques using Jupyter Notebooks.

Linear Regression: Multicollinearity, Outliers, Model Specification

Covers multicollinearity, outliers, model specification, and practical strategies in linear regression.

Measures of central tendency

Covers mean, median, mode, box plots, and histograms in datasets.

Extreme Values Detection

Covers methods for identifying and handling extreme values in data, including statistical procedures for outlier detection.

System of ODEs: High Order ODEs

Covers high order ODEs, numerical methods, and stability criteria.

Statistical Analysis: Boxplot and Normal Distribution

Introduces statistical analysis concepts like boxplot and normal distribution using real data examples.

ANOVA and Factorial Experiments

Explores ANOVA, factorial experiments, and model evaluation techniques.

Descriptive Statistics: Hypothesis Testing

Introduces descriptive statistics, hypothesis testing, p-values, and confidence intervals, emphasizing their importance in data analysis.

Matlab: 3D Surface Plotting

Covers logical arrays, 3D surface plotting, parametric curves, interpolation, and fitting in Matlab.

Logistic Regression: Interpretation & Feature Engineering

Covers logistic regression, probabilistic interpretation, and feature engineering techniques.

Linear Regression Testing

Explores least squares in linear regression, hypothesis testing, outliers, and model assumptions.

Types of Variables and Multinomial Distribution

Introduces types of variables, multinomial distribution, data characteristics, shapes of densities, correlation, and data visualization methods.

Linear Regression: Statistical Inference and Regularization

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

Exploratory Statistics: Data Analysis Fundamentals

Covers the basics of exploratory statistics, including variables, quantiles, central tendency, dispersion, outliers, and robustness.

Robust Regression: Methods and Applications

Explores robust and resistant methods in linear models, emphasizing the importance of handling extreme observations and the implications of robustness in regression models.