Studies and Group Work

Related lectures (30)

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Explores conditional probability, Framingham studies, effect size, t-test, and sampling error in statistics.

Covers generative models, focusing on Boltzmann machines and constrained maximization using Lagrange multipliers.

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

Explores data representations, overfitting, model selection, Bag of Words, and learning with imbalanced data.

Explores challenges in natural language generation, decoding algorithms, training issues, and reward functions.

Explores data representations, overfitting, model selection, cross-validation, and imbalanced data challenges.

Covers determinantal point processes, sine-process, and their extrapolation in different spaces.

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

Covers the Fourier transform of sampled signals, reconstruction, and harmonic response.

Explores constructing a probability model, random sampling, variance calculation, and allocation optimization in experiments.