Lectures related to Bernoulli trial

Probability: Bernoulli Trials

Explores Bernoulli trials, independent trials, and the binomial distribution.

Random Variables: Expected Value

Covers advanced probability concepts, including random variables and expected value calculation.

Advanced Probability: Expected Value

Explores expected value in probability theory, including dice rolls and Bernoulli trials.

Biclustering & latent variables: statistical analysis of network data

Explores biclustering techniques and latent variables in network data analysis.

Variance: Definition, Examples, and Theorems

Covers the definition of variance, examples, theorems, and applications in probability theory.

Random Variables: Basics and Examples

Explains random variables, distributions, and Bernoulli trials with coin flip examples.

Discrete Random Variables: Functions and Probabilities

Explores discrete random variables, their functions, and probabilities in various scenarios.

Parameter Estimation

Discusses parameter estimation, including checks, quality, distribution, and statistical properties of estimates.

Advanced Probability: Probability Trees and Conditional Probabilities

Explores probability trees, conditional probabilities, Bernoulli trials, binomial distribution, and Bayes' Theorem.

Variance and Independent Random Variables

Covers variance, independent random variables, and their properties, including examples and proofs.

Confidence Intervals: Student, Asymptotic Wald

Covers confidence intervals for Gaussian means, Student distribution, and Wald confidence intervals for maximum likelihood estimators.

Infinite Coin Tosses: Independence

Explores independence in infinite coin tosses, covering sets, shifts, and T-invariance.

Random Variables and Expected Value

Introduces random variables, probability distributions, and expected values through practical examples.

Advanced Probabilities: Random Variables & Expected Values

Explores advanced probabilities, random variables, and expected values, with practical examples and quizzes to reinforce learning.

Maximum Likelihood Estimation: Theory and Examples

Covers maximum likelihood estimation, including the Rao-Blackwell Theorem proof and practical examples of deriving estimators.

Probability and Statistics

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

Advanced Probability

Delves into advanced probability theory, covering inequalities, trials, distributions, and calculations for probabilities and expectations.

Probability: Quiz

Covers probability scenarios and includes a quiz using Kahoot.

Sufficient Statistics: Understanding Data Compression

Explores sufficient statistics, data compression, and their role in statistical inference, with examples like Bernoulli Trials and exponential families.

Supervised Learning Fundamentals

Introduces the fundamentals of supervised learning, including loss functions and probability distributions.