Lectures related to Assumption of Machine Learning

Explores research process, variable types, causality vs correlation, and sampling strategies.

Covers concentration inequalities and sampling methods for estimating unknown distributions, with a focus on population infection rates.

Explores efficient stochastic numerical methods for modeling and learning, covering topics like the Analytical Engine and kinase inhibitors.

Covers the basics of machine learning for physicists and chemists, focusing on image classification and dataset labeling.

Covers linear regression, gradient descent, overfitting, and ridge regression among other concepts.

Explores regularization, learning algorithms, and subgaussian assumptions in machine learning.

Illustrates sampling strategies with simple random and stratified sampling on a 1000K population.

Covers a simple mathematical model for supervised learning with k-nearest neighbors in regression.

Covers sampling, cross-validation, quantifying performance, optimal model determination, overfitting detection, and classification sensitivity.

Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.

Introduces supervised learning concepts and the k-Nearest Neighbors method for classification and regression tasks.

Focuses on resolving real estate law exam questions and legal principles for effective exam preparation.

Covers statistical physics tools for optimization, learning, graph coloring, recommendation systems, and neural networks.

Explores challenges in data assumptions, biases, and more in research, including incomplete write-ups and frustrations of newcomers.

Covers the process of making pizza, sampling, averages, dispersion, residuals, and normal distribution.

Explores sampling, inferential statistics, and effective experimentation in statistics.

Explores sampling in inferential statistics, emphasizing the impact of sample size and randomness on inference accuracy.

Explores model assessment metrics, selection techniques, bias-variance tradeoff, and handling skewed data distributions in machine learning.

Covers the fundamentals of machine learning for physicists and chemists, focusing on image classification tasks using artificial intelligence.

Covers the characteristics and importance of the normal distribution, including examples and treatment scenarios.