Directional selection

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Related lectures (32)

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Generalized Linear Models

Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.

Newton's Method: Convergence

Explores the convergence of Newton's method for solving nonlinear equations and the importance of selecting appropriate initial guesses.

Discrete choice and machine learning: two methodologies

Delves into the complementary methodologies of discrete choice and machine learning, covering notations, variables, models, data processes, extrapolation, what-if analysis, and more.

Risk Estimation: Mallows' CL and Cp

Discusses optimism in risk estimation, effective degrees of freedom, and Mallows' CL and Cp for linear estimators.

Kinematics: Curvilinear Abscissa

Covers curvilinear abscissa in kinematics, discussing positive direction and duration comparison.

Model Evaluation and Selection

Discusses model evaluation, cross-validation, and variable selection in machine learning.

Stéréotomie: Vousssoirs and Collaborative Work

Covers the organization of the course, creation of voussoir components, collaborative work, and selection of group components.

Random Walks and Moran Model in Population Genetics

Explores random walks, Moran model, bacterial chemotaxis, entropy, information theory, and coevolving sites in proteins.

Drug Discovery: Computational Approaches

Explores the EXSCALATE4COV project, focusing on computational drug discovery for COVID-19 treatments and the collaboration between academia and industry.

Active Remote Sensing: Principles and Applications

Explains the principles of active remote sensing, focusing on synthetic aperture radar and its applications in environmental monitoring.