Lectures related to Open data | EPFL Graph Search

Introduction to Renku

Introduces Renku, a platform for collaborative data science, emphasizing reproducibility, shareability, reusability, and security.

Diffusion-Convection: Modeling and Schemes

Covers modeling and numerical schemes for diffusion-convection problems.

Veridical Data Science: Responsible, Reliable, Reproducible

By Prof. Bin Yu explores veridical data science, emphasizing responsible, reliable, and reproducible data analysis and decision-making.

Urban Metabolism Analysis

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

Block Pulled by a Spring: Dynamics

Covers the dynamics of a block connected to a spring, deriving equations of motion and solving for key time points.

Micromagnetic Simulations

Explores magnetic simulations at various scales, from micromagnetism to atomistic spin dynamics.

Principal Component Analysis: Dimension Reduction

Covers Principal Component Analysis for dimension reduction in biological data, focusing on visualization and pattern identification.

Leveraging Big Data Analytics

Explores key strategies for using big data analytics in managing urban infrastructures.

Differential Equations and Heat Diffusion

Covers differential equations, heat diffusion, and advanced analysis for engineers.

Architecture Trade-offs in a Planet-scale Queueing System

Explores the trade-offs in a planet-scale queueing system, emphasizing the importance of relaxing semantics to manage complexity.

Big Data Ecosystems: Technologies and Challenges

Covers the fundamentals of big data ecosystems, focusing on technologies, challenges, and practical exercises with Hadoop's HDFS.

Implementation Research: Needs Assessment

Emphasizes the importance of needs assessment in implementation research using various methods and data sources.

Bulky Hamiltonian isotopies

Explores the concept of bulky Hamiltonian isotopies of Lagrangian tori.

Distributed Computing: Challenges and Solutions

Explores challenges in distributed computing, data growth, and data types, emphasizing the battle against the three Vs in big data.

Deep Learning Accelerators: Optimization Strategies

Explores optimization strategies for deep learning accelerators, emphasizing data movement reduction through batching, dataflow optimizations, and compression.

Model Database Resolution

Covers model database resolution, hydrology, GIS, connections, transitions, and validation using various datasets.

Data Ingestion Layer: SmartDataLake

Explores the data ingestion process in SmartDataLake, including datasets and the RAW platform.

Unsupervised Learning: Clustering & Dimensionality Reduction

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

River Hydraulics and Modeling: Semi-Distributed Approach

Explores river hydraulics, modeling, and calibration using a semi-distributed approach for accurate forecasting and water resource management.

Maximum Likelihood Inference

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.