Data Collection and Preparation

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Related lectures (32)

Page 2 of 4

Gender, Sex & Intersectionality: Chapter 2

Explores the significance of intersectional gender analysis in implementation research for diseases of poverty, emphasizing transformative goals and inclusivity.

Urban Metabolism Analysis

Explores systemic environmental assessment, national material flow analysis, and urban metabolism dashboard development for Zurich using open data.

Deep Learning: Data, Models, and Challenges

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

Data Issues in Research

Explores challenges in data assumptions, biases, and more in research, including incomplete write-ups and frustrations of newcomers.

Data Driven Science: MODNet Methodology

Explores the MODNet methodology for material property predictions, emphasizing feature selection and supervised learning.

Data Annotation: Collection and Biases in NLP

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

Critical Data Studies: Introduction and Models

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

Machine Learning and Privacy

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

Machine Learning: Features and Model Selection

Delves into the significance of features, model evolution, labeling challenges, and model selection in machine learning.

Imaging Techniques: Geometric and Radiometric Distortions

Discusses imaging techniques, focusing on geometric and radiometric distortions in aerial photography and their implications for data interpretation.