Lectures related to Theory of Bagging

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.

Equivalences of Categories

Explores examples of natural transformations, equivalence of categories, and adjunction with specific instances involving Un.

Homotopical Algebra

Covers the theory of groups and homotopical algebra, emphasizing natural transformations, identities, and isomorphism of categories.

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

Enterprise and Service-Oriented Architecture: Team Creation and Meeting Preparation

Covers key learning points, team creation, meeting preparation, and the theory behind interviews.

The Role of Symmetries

Delves into symmetries in physics, covering group theory, perturbation, and quantification.

Quantum Field Theory: Fermions and Grassmann Numbers

Explores quantum field theory, focusing on fermions and Grassmann numbers in the path integral formalism.

Covalent Bonding: Orbital Stability

Explores covalent bonding stability and orbital properties in molecules like H₂ and He.

Group Theory: Adjoint Functors and G-sets

Explores adjunction between functors, composition of applications, G-equivariance, and natural transformations in G-sets.

Machine Learning Fundamentals: Structure Discovery, Classification, Regression

Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.

Linear Applications and Simple Objects

Covers the bijection between linear applications from L(X) to V and applications from X to U(V).