Lecture
Data Science and Education at EPFL
Related lectures (35)
Introduction to R Programming for Genetics & Genomics
Introduces a course on Genetics & Genomics, focusing on R programming with interactive exercises.
Atomistic Machine Learning: Physics and Data
Explores Atomistic Machine Learning, integrating physical principles into models to predict molecular properties accurately.
Vision-Language-Action Models: Training and Applications
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.
Block Pulled by a Spring
Explores the dynamics of a block pulled by a spring under various conditions.
Transport Equation: Numerical Analysis
Covers optimization, control problems, and neural networks in the context of the transport equation.
Neural Networks: Training and Activation
Explores neural networks, activation functions, backpropagation, and PyTorch implementation.
Logistic Regression: Vegetation Prediction
Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.
Machine learning: Physics and Data
Delves into the intersection of physics and data in machine learning models, covering topics like atomic cluster expansion force fields and unsupervised learning.
Phenomenological Applications of the Standard Model
Explores the verification of the standard model through scattering experiments and the implications of minimal dark matter models.
Machine Learning Fundamentals
Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.