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Related lectures (29)

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

Probability and Statistics

Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.

Probability and Statistics: Fundamentals

Covers the fundamental concepts of probability and statistics, including interesting results, standard model, image processing, probability spaces, and statistical testing.

Linear Regression: Estimation and Testing

Explores linear regression estimation, hypothesis testing, and practical applications in statistics.

Statistical Analysis: Boxplot and Normal Distribution

Introduces statistical analysis concepts like boxplot and normal distribution using real data examples.

Linear Regression: Ozone Data Analysis

Explores linear regression analysis of ozone data using statistical models.

Probability and Statistics

Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.

State Space Models: Expressivity of Transformers

Covers state space models and the expressivity of transformers in sequence copying tasks.

Gaussian Naive Bayes & K-NN

Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.

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