Modern NLP: Classical Language Models

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Probability and Statistics: Fundamentals

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

Probability and Statistics

Covers probability, statistics, independence, covariance, correlation, and random variables.

Words and Tokens: Language Models and Probabilities

Reviews language models, tokenization, and probability estimation in NLP systems.

Probability Basics: Theory and Applications

Covers basic probability theory, conditional probability, independence, and Bayes' Theorem.

Law of Large Numbers: Strong Convergence

Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.

Probability and Statistics

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

Conditional Probability: Understanding Events and Their Relationships

Covers conditional probability, illustrating how events influence each other's probabilities and its applications in real-world scenarios.

Central Limit Theorem: Properties and Applications

Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.

Variance and Covariance: Properties and Examples

Explores variance, covariance, and practical applications in statistics and probability.