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Concept

Feature selection

Related lectures (32)

Linear Regression: Statistical Inference and Regularization

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

Model Selection: Least Squares

Explores model selection in least squares regression, addressing multicollinearity challenges and introducing shrinkage techniques.

Machine Learning Basics

Introduces machine learning basics, including data collection, model evaluation, and feature normalization.

LASSO Regression: Sparse Signal Induction

Explores LASSO regression for inducing sparsity in signals through gradient descent.

Model Building: Linear Regression

Explores model building in linear regression, covering techniques like stepwise regression and ridge regression to address multicollinearity.

Ridge Regression: Penalised Least Squares

Explores Ridge Regression for handling multicollinearity and the LASSO method for model selection.

Data Driven Science: MODNet Methodology

Explores the MODNet methodology for material property predictions, emphasizing feature selection and supervised learning.

Applied Machine Learning: Features and Models

Explores data collection, feature selection, model building, and performance evaluation in machine learning, emphasizing feature engineering and model selection.

Model Selection Criteria: AIC, BIC, Cp

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Statistical Thermodynamics: Density of States

Explores density of states in statistical thermodynamics and the use of Heaviside functions for energy level probabilities.

Statistical Thermodynamics: Particles and Levels

Covers statistical thermodynamics of particles and levels, permutations, and entropy in rubber band elasticity.

Statistical Thermodynamics: Energy States and Convolution

Covers energy states and convolution in statistical thermodynamics, with an example on Benzene.

Statistical Thermodynamics: Phase Space and Probability Distributions

Covers phase space, ensembles of particles, probability distributions, and probability density functions.

Statistical Thermodynamics: Degeneracies and Laplace Transform

Covers degeneracies in statistical thermodynamics and the Laplace transform.

Bayesian Parameter Estimation

Explores Bayesian Parameter Estimation, emphasizing cost minimization and reward maximization.

Statistical Thermodynamics: Partition Functions and Quantum Statistics

Introduces partition functions, quantum statistics, and the Einstein model for molecular behavior.

Landscape and Generalisation in Deep Learning

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Other regularizations + the Lasso

Explores diverse regularization approaches, including the L0 quasi-norm and the Lasso method, discussing variable selection and efficient algorithms for optimization.

Avoiding Implicit Bias in Hiring

Discusses blind auditions, selection criteria, and recommendation letters in hiring to prevent gender bias.

PhD Programs at EPFL

Covers PhD programs at EPFL, including selection procedures, research areas, and support for students.