Data Science Foundations

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Related lectures (29)

Page 2 of 3

Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.

Large Deviations: Theory and Applications

Delves into large deviations in statistical mechanics, exploring Kramer's theorem, Varadhan's lemma, and Laplace's method.

Probability Models: Fundamentals

Introduces the basics of probability models, covering random variables, distributions, and statistical estimation.

Advanced Probability: Summary

Covers random variables, sample spaces, probability distributions, functions, expected value, variance, and estimations.

Probability and Statistics

Explores joint random variables, conditional density, and independence in probability and statistics.

Statistical Theory: Fundamentals

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.

Probabilities and Statistics

Covers fundamental concepts in probabilities and statistics, including linear regression, exploratory statistics, and the analysis of probabilities.

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.

Normal Distribution: Properties and Calculations

Covers the normal distribution, including its properties and calculations.

Continuous Random Variables: Distributions and Examples

Explores continuous random variables, density functions, and distribution laws with practical examples.