Funding an Idea: Sources and Steps

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Related lectures (30)

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Estimating Extreme Quantiles

Covers the estimation of extreme quantiles using empirical quantiles and sample data.

Hypothesis Testing and Confidence Intervals: An Overview

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

Data Representations & Processing

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

Introduction to Machine Learning: Course Overview and Basics

Introduces the course structure and fundamental concepts of machine learning, including supervised learning and linear regression.

Gaussian Mixture Models: Data Classification

Explores denoising signals with Gaussian mixture models and EM algorithm, EMG signal analysis, and image segmentation using Markovian models.

Data Representations & Processing

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

Overview of Particle Physics and Rutherford Scattering

Covers the constituents of matter, fundamental forces, the Standard Model, natural units, and particle interaction experiments.

Total scattering and PDF analysis

Explores total scattering and PDF analysis in materials science, covering in-situ synthesis, data analysis techniques, and applications in host-guest systems.

Exploratory Statistics: Understanding Populations and Samples

Covers exploratory statistics, focusing on populations, samples, and various statistical measures.

Model Assessment: Metrics and Selection

Explores model assessment metrics, selection techniques, bias-variance tradeoff, and handling skewed data distributions in machine learning.