Lectures related to Feature selection

Linear Regression: Statistical Inference and Regularization

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

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.

Feature Selection

Explores feature selection methods, pitfalls, and normalization techniques for optimal model performance.

Supervised Learning: Regression Methods

Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

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.

Decision Trees: Induction and Pruning

Explores Decision Trees, from induction to pruning, emphasizing interpretability and automatic feature selection strengths, while addressing challenges like overfitting.

Variable Selection Methods: Subset vs. Container Approaches

Explores variable selection methods in machine learning, including subset and container approaches, using exhaustive and greedy procedures.

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.

Principal Component Analysis: Dimension Reduction

Explores Principal Component Analysis for dimension reduction in datasets and its implications for supervised learning algorithms.

Bayesian Parameter Estimation

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