Lectures related to Untitled | EPFL Graph Search

Probability and Statistics

Introduces probability, statistics, distributions, inference, likelihood, and combinatorics for studying random events and network modeling.

Introduction to Convexity

Introduces the key concepts of convexity and its applications in different fields.

Statistical Models: Basics & Applications

Covers statistical concepts like probability, estimation, hypothesis testing, and confidence intervals for mathematicians.

Combinatorial Mathematics

Explores combinatorial mathematics, covering permutations, combinations, and binomial coefficients, along with probability and statistics concepts.

Statistical Inference: Approximate Critical Values and Confidence Intervals

Covers the construction of confidence intervals and approximate critical values in statistical inference.

Probability and Statistics

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

Confidence Intervals: Definition and Estimation

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Fundamental Limits of Gradient-Based Learning

Delves into the fundamental limits of gradient-based learning on neural networks, covering topics such as binomial theorem, exponential series, and moment-generating functions.

Permutations and Binomial Coefficients

Explains permutations, binomial coefficients, and multinomial coefficients with practical examples.

Statistical Significance: Maximum Likelihood Estimation and Confidence Intervals

Explores type I and type II errors, critical values, and confidence intervals in statistical significance.

Continuous Random Variables: Basic Ideas

Explores continuous random variables and their properties, including support and cumulative distribution functions.

Linear Regression: Estimation and Inference

Explores linear regression estimation, linearity assumptions, and statistical tests in the context of model comparison.

Probability Theory: Midterm Solutions

Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.

Estimators and Confidence Intervals Log in to Mediaspace to watch this video

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Stochastic Models for Communications Log in to Mediaspace to watch this video

Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.

Spin Glasses and Bayesian Estimation Log in to Mediaspace to watch this video

Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

Generalized Linear Models: A Brief Review Log in to Mediaspace to watch this video

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

Confidence Intervals and Hypothesis Testing Log in to Mediaspace to watch this video

Explores confidence intervals, hypothesis testing, and decision-making using test statistics and p-values.

The Marriage Problem Log in to Mediaspace to watch this video

Explores the marriage problem, modeling the process as a controlled stochastic process with dynamic programming algorithms to find the optimal policy for accepting bachelors.

Optimal Stopping Problems: Theory and Applications Log in to Mediaspace to watch this video

Covers optimal stopping problems in applied probability and stochastic processes, focusing on theory and practical applications.