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Lecture
Statistical Modelling & Probability Basics
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Related lectures (29)
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Probability and Statistics: Fundamental Theorems
Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.
Law of Large Numbers: Strong Convergence
Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.
Probabilistic Linear Regression
Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.
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.
Introduction to Inference
Covers the basics of probability theory, random variables, joint probability, and inference.
Probability and Statistics
Covers p-quantile, normal approximation, joint distributions, and exponential families in probability and statistics.
Conditional Density and Expectation
Explores conditional density, expectations, and independence of random variables with practical examples.
Dependence and Correlation
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Probability Theory: A Review
Offers a review of key probability concepts, including probability spaces, events, and random variables.
Linear Combinations: Moment-Generating Functions
Explores moment-generating functions, linear combinations, and normality of random variables.
