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Related lectures (32)
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Compression: Prefix-Free Codes
Explains prefix-free codes for efficient data compression and the significance of uniquely decodable codes.
Linear Algebra in Data Science
Explores the application of linear algebra in data science, covering variance reduction, model distribution theory, and maximum likelihood estimates.
Central Limit Theorem: Properties and Applications
Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.
Measures of central tendency
Covers mean, median, mode, box plots, and histograms in datasets.
Law of Large Numbers: Strong Convergence
Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.
Linear Algebra: Projection and Rotation
Covers projection, rotation, white Gaussian noise, and waveform observation in linear algebra.
Sufficient Statistics: Understanding Data Compression
Explores sufficient statistics, data compression, and their role in statistical inference, with examples like Bernoulli Trials and exponential families.
Describing Data: Statistics and Hypothesis Testing
Covers descriptive statistics, hypothesis testing, and correlation analysis with various probability distributions and robust statistics.
Model Selection Criteria: AIC, BIC, Cp
Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.
Elements of Statistics
Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.
