Lecture

Studies and Group Work

Related lectures (30)

Explores sampling in inferential statistics, emphasizing the impact of sample size and randomness on inference accuracy.

Statistical Analysis: Data Exploration and Inference

Covers statistical analysis, emphasizing data exploration and inference to quantify uncertainty and draw conclusions.

Density of States and Bayesian Inference in Computational Mathematics

Explores computing density of states and Bayesian inference using importance sampling, showcasing lower variance and parallelizability of the proposed method.

Hypothesis Testing and Confidence Intervals: Key Concepts

Provides an overview of hypothesis testing and confidence intervals in statistics, including practical examples and key concepts.

Experimental Design and Analysis

Covers the basics of experimental design and analysis, focusing on statistical techniques like ANOVA, regression, mediation, and moderation.

Approximate Query Processing: BlinkDB

Introduces BlinkDB, a framework for approximate query processing using sampling techniques.

Sampling: Inference and Statistics

Explores sampling, inferential statistics, and effective experimentation in statistics.

Ceramics: Powder Characterization

Explores the characterization of powders in ceramics, emphasizing the impact on ceramic properties and the manufacturing process.

Intersectional Gender Perspective: Implementation Research

Discusses the significance of integrating an intersectional gender perspective in implementation research for infectious diseases of poverty.

Sampling strategies

Explores research process, variable types, causality vs correlation, and sampling strategies.

Statistics & Experimental Design

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

Generative Models: Boltzmann Machine

Covers generative models, focusing on Boltzmann machines and constrained maximization using Lagrange multipliers.

Metrics for Classification

Covers sampling, cross-validation, quantifying performance, optimal model determination, overfitting detection, and classification sensitivity.

Data Representations & Processing

Explores data representations, overfitting, model selection, Bag of Words, and learning with imbalanced data.

Natural Language Generation: Decoding & Training

Explores challenges in natural language generation, decoding algorithms, training issues, and reward functions.

Data Representations & Processing

Explores data representations, overfitting, model selection, cross-validation, and imbalanced data challenges.

Determinantal Point Processes and Extrapolation

Covers determinantal point processes, sine-process, and their extrapolation in different spaces.

Pizza Making Process

Covers the process of making pizza, sampling, averages, dispersion, residuals, and normal distribution.

Fourier Transform and Sampling

Covers the Fourier transform of sampled signals, reconstruction, and harmonic response.

Probability Model Construction

Explores constructing a probability model, random sampling, variance calculation, and allocation optimization in experiments.