Naive Bayes Classifier

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Related lectures (29)

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Supervised Learning: Likelihood Maximization

Covers supervised learning through likelihood maximization to find optimal parameters.

Probability: Examples

Covers examples of probability, including Bayes Theorem, independence, and conditional probability.

Generalized Bayes Theorem: Vaccines and Population Probability

Explores the Generalized Bayes Theorem with three vaccines and population probabilities.

Random Vectors: Stochastic Models for Communications

Covers random vectors, joint probability, and Gaussian random variables in communication models.

Probability Fundamentals

Introduces fundamental probability concepts, including events, complements, conditional probability, and random variables.

Probability and Statistics: Independence and Conditional Probability

Explores independence and conditional probability in probability and statistics, with examples illustrating the concepts and practical applications.

Probabilities: Fundamentals and Independence

Covers the fundamentals of probabilities, including independence, conditional probabilities, and random variables.

Statistical Inference and Machine Learning

Covers statistical inference, machine learning, SVMs for spam classification, email preprocessing, and feature extraction.

Entropy and Data Compression: Huffman Coding Techniques

Discusses entropy, data compression, and Huffman coding techniques, emphasizing their applications in optimizing codeword lengths and understanding conditional entropy.