Assumption of Machine Learning

Related lectures (32)

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

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

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

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

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.

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Covers the Boltzmann Machine, a type of stochastic recurrent neural network.