Introduction to Computer Science: Three Domains

Related lectures (31)

Page 2 of 4

By Prof. François Marechal covers the simulation and optimization of energy systems in industrial processes, waste, resources, and energy conversion.

Covers the integration of machine learning with scanning probe microscopy for enhanced automation and efficiency in scientific workflows.

Covers optimization and simulation techniques for drawing from multivariate distributions and dealing with correlations.

Explores statistical analysis, mean square error, and bootstrapping methods in optimization and simulation.

Explores the modeling of the '100 prisoners' problem and compares naive and optimal approaches.

Explores the history and tools of scientific computing in neuroscience, emphasizing the simulation of neurons and networks.

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

Covers advanced data wrangling techniques using Hadoop, focusing on Hive and HBase integration.

Explores the evolution from data analysis to AI and ML, emphasizing big data, machine learning, and social media interaction.

Covers algorithmic thinking, Python programming, numerical methods, and essential computing concepts for scientific computing.