Lectures related to Z-test | EPFL Graph Search

Explores confidence intervals, T-test, and hypothesis testing, including assumptions and critical regions.

Introduces hypothesis testing, focusing on the Z-test and critical regions.

Explores conditional probability, Framingham studies, effect size, t-test, and sampling error in statistics.

Introduces the t-test for assessing categorical effects on quantitative outcomes, covering hypothesis testing, assumptions, and alternative tests.

T-tests and Empirical Tests

Explores t-tests, z-tests, and empirical tests for sample comparison and hypothesis testing.

Design and Analysis of Experiments

Covers the design and analysis of experiments, focusing on statistics for experimenters.

Heat Equation: Diffusion

Covers the heat equation for diffusion and conservation of thermal energy.

Hypothesis Testing and Confidence Intervals: An Overview

Covers hypothesis testing, confidence intervals, and their applications in statistics.

Introduction to t tests

Covers the basics of t tests, including hypothesis testing, significance levels, P-values, Z-tests, and the t distribution.

Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Genomic Data Analysis: Identifying Differentially Expressed Genes

Discusses methods for identifying differentially expressed genes in genomic data analysis.

Evaluation in NLP

Delves into NLP evaluation, covering gold standards, precision, recall, and statistical significance.

Statistical Tests: T-Tests and ANOVA

Covers the calculation of paired t-tests, advantages/disadvantages of different t-tests, and the concept of ANOVA.

Synchronization

Covers the principles of synchronization in parallel computing, focusing on shared memory synchronization and different methods like locks and barriers.

Statistical Modeling: Estimation and Hypothesis Testing

Covers statistical modeling, estimation, CLT, confidence intervals, and hypothesis testing steps with practical examples.

Model Assessment and Hyperparameter Tuning

Explores model assessment, hyperparameter tuning, and resampling strategies in machine learning.

Hypothesis Testing: T-Test Methodology

Explores hypothesis testing methodology using t-tests and the randomization test.

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Hypothesis Testing: Statistics Overview

Provides an overview of hypothesis testing, p-values, Wald test, and non-parametric statistics.

Synchronization: Hardware Implementation

Explores hardware synchronization methods, including locks, barriers, and critical sections in parallel computing.