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Related lectures (32)

Page 2 of 4

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.