Theory of Bagging

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (27)

Page 1 of 3

Machine Learning at the Atomic Scale

Explores simple models, electronic structure evaluation, and machine learning at the atomic scale.

Natural Transformations: Functors and Categories

Explores functors, natural transformations, and the theory of groups, emphasizing the importance of comparisons and structure preservation.

Introduction to Reinforcement Learning: Key Concepts and Applications

Introduces reinforcement learning, covering its definitions, applications, and theoretical foundations, while outlining the course structure and objectives.

Scientific Machine Learning: Applications and Algorithms

Explores scientific machine learning applications, challenges with sparse data, and physics-inspired algorithms to improve spectral methods.

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Stochastic Gradient Descent: Theory and Applications

Covers the theory and applications of Stochastic Gradient Descent in machine learning models.

Introduction to Convexity

Introduces the key concepts of convexity and its applications in different fields.

Active Learning Session: Group Theory

Explores active learning in Group Theory, focusing on products, coproducts, adjunctions, and natural transformations.

Hartree-Fock Equations for N Fermions

Discusses the minimization of the Hartree-Fock functional for N fermions and the variational principle in quantum physics.

Active Learning: Group Theory

Explores adjoint functors, points fixes, orbits, and non-trivial actions in group theory.