Modeling the Pandemic: Challenges and Responses

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Related lectures (32)

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Critical Data Studies: Introduction and Models

Introduces the Critical Data Studies course, covering data models, trust, reproducibility, biases, and ethics.

Data Annotation: Collection and Biases in NLP

Addresses data collection, annotation processes, and biases in natural language processing.

Types of Variables and Multinomial Distribution

Introduces types of variables, multinomial distribution, data characteristics, shapes of densities, correlation, and data visualization methods.

Machine Learning for Behavioral Data

Introduces a course on Machine Learning for Behavioral Data at EPFL, covering ML algorithms, data handling, and model evaluation.

Machine Learning Basics

Introduces machine learning basics, including data collection, model evaluation, and feature normalization.

Tracking Built-in Events in Google Analytics 4

Explains how to verify and activate built-in events in Google Analytics 4.

Machine Learning and Privacy

Delves into machine learning's impact on privacy, discussing attacks, vulnerabilities, and ethical considerations in data usage.

Inference: Poisson Regression

Covers iterative weighted least squares, model checking, Poisson regression, and fitting multinomial models using Poisson errors.

Deep Learning: Data, Models, and Challenges

Provides an overview of deep learning concepts, focusing on data, model architecture, and challenges in handling large datasets.

Modern NLP: Project Description

Presents a project description for building educational chatbots using ChatGPT-like models, outlining stages, policies, and data sharing for research purposes.