Trade-offs in Data and Time

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (27)

Page 2 of 3

L1 Regularization: Sparse Solutions and Dimensionality Reduction

Delves into L1 regularization, sparse solutions, and dimensionality reduction in the context of machine learning.

Numerical Differentiation: Part 1

Covers numerical differentiation, forward differences, Taylor's expansion, Big O notation, and error minimization.

Optimization with Constraints: KKT Conditions

Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.

Numerical Analysis: Nonlinear Equations

Covers algorithms for solving mathematical problems approximately using a computer, including nonlinear equations and numerical approximation methods.

Numerical Methods: Stopping Criteria, SciPy, and Matplotlib

Discusses numerical methods, focusing on stopping criteria, SciPy for optimization, and data visualization with Matplotlib.

Scientific Computing in Neuroscience

Explores scientific computing in neuroscience, emphasizing the simulation of neurons and networks using tools like NEURON, NEST, and BRIAN.

Numerical Integration: Introduction to SciPy and Matplotlib

Covers numerical integration techniques using SciPy and Matplotlib for visualizing functions and approximating integrals.

Computational Science and Engineering: Master Program Overview

Outlines the Master in Computational Science and Engineering program at EPFL, detailing its structure, projects, and career opportunities for graduates.

Optimization with Constraints: Theory and Applications

Covers the theory and applications of optimization with constraints, including key concepts and numerical methods.

Numerical Methods: Exercises

Covers exercises related to numerical methods, focusing on computational algorithms and problem-solving strategies.