Urban Metabolism Analysis

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Related lectures (32)

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Introduction to Machine Learning: Course Overview and Basics

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

Advanced Pandas Functions

Focuses on advanced pandas functions for data manipulation, exploration, and visualization with Python, emphasizing the importance of understanding and preparing data.

Material and Energy Flow Analysis for Resource Management

Covers Material and Energy Flow Analysis for resource management, urban systems, sustainable urban transition, and resource use dynamics.

Data Wrangling with Hive: Managing Big Data Efficiently

Covers data wrangling techniques using Apache Hive for efficient big data management.

Big Data Best Practices and Guidelines

Covers best practices and guidelines for big data, including data lakes, architecture, challenges, and technologies like Hadoop and Hive.

Data Science Essentials

Covers the essentials of data science, including data handling, visualization, and analysis, emphasizing practical skills and active engagement.

Gaussian Mixture Models: Data Classification

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

Urban Metabolism and Circular Economy

Explores urban metabolism, circular economy, accounting approaches, and policy measures for sustainable resource management in cities.

Material and Energy Flow Analysis

Covers Material and Energy Flow Analysis for resource management, focusing on methodologies and examples from cities like Vienna and Paris.

Logistic Regression: Vegetation Prediction

Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.