Data Representation: Models and Systems

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Covers the representation of mathematical models, functions, and data structures in information systems.

Covers Distributed Information Systems, key tasks, methods, projects, evaluation, and exam support.

Introduces the course on information systems, covering its structure, objectives, and foundational concepts essential for understanding data management and decision-making.

Covers the overview of information systems, including real-world models, data types, and model evaluation.

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

Explores data representation, databases, cloud computing, and challenges in the cloud environment.

Explores knowledge representation, data structures, semantics, and the challenges of searching for data on the web.

Covers the overview of information systems, data modeling, managing data, and the distinction between data and information.

Introduces a course on Genetics & Genomics, focusing on R programming with interactive exercises.

Explores the definition and components of information systems, emphasizing their role in collecting, processing, and distributing information.

Delves into training and applications of Vision-Language-Action models, emphasizing large language models' role in robotic control and the transfer of web knowledge. Results from experiments and future research directions are highlighted.

Introduces fundamental concepts of data management, including data models, databases, and key tasks.

Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Covers fundamental programming concepts such as algorithms, variables, and expressions in C++.

Covers Python basics, Pandas, data manipulation, visualization, and machine learning.

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Introduces the Applied Data Analysis course at EPFL, covering a broad range of data analysis topics and emphasizing continuous learning in data science.

Explores neurophysiological data analysis, covering AP identification, firing rates, subthreshold activity, FFT spectral analysis, and event-triggered analysis using MATLAB.