Lectures related to Normal Ordered Product And Wick Theorem

Feynman Rules I: Asymptotic Statistic and Instantons

Covers the Feynman Rules, Asymptotic Statistics, Normal Ordering, and Instantons.

Quantum Field Theory II: Cross Section & Lifetime

Covers cross section, lifetime, quantum fluid, asymptotic states, discrete symmetries, and normal ordering in quantum field theory.

Normal Ordered Product And Wick Theorem

Covers normal ordered product and Wick's theorem in quantum field theory.

Quantum Fluids: Cross Section & Lifetime

Covers the concepts of cross section and lifetime in quantum fluids, focusing on transition and differential probabilities.

Linear Recurrence Relations

Explores linear recurrence relations, including examples like the Fibonacci numbers and the proof of related theorems.

Characteristic Polynomials and Similar Matrices

Explores characteristic polynomials, similarity of matrices, and eigenvalues in linear transformations.

Eigenstate Thermalization Hypothesis

Explores the Eigenstate Thermalization Hypothesis in quantum systems, emphasizing the random matrix theory and the behavior of observables in thermal equilibrium.

QED Calculations: Matrix Elements and Cross Sections

Explores QED calculations, Feynman diagrams, matrix elements, interference terms, cross sections, electron-positron annihilation, spin considerations, and chiral eigenstates.

Multivariate Statistics: Wishart and Hotelling T²

Explores the Wishart distribution, properties of Wishart matrices, and the Hotelling T² distribution, including the two-sample Hotelling T² statistic.

Linear Algebra: Abstract Concepts

Introduces abstract concepts in linear algebra, focusing on operations with vectors and matrices.

Matrix Operations: Product and Inverse

Covers matrix operations, focusing on the product and inverse of matrices.

Stein Algorithm: Polynomial Identity Testing

Explores the Stein algorithm for polynomial identity testing and the minimization of a cut problem.

Eigenvalues and Minimal Polynomial

Explores eigenvalues and minimal polynomial, emphasizing their importance in linear algebra.

Graphical Models: Representing Probabilistic Distributions

Covers graphical models for probabilistic distributions using graphs, nodes, and edges.

Linear Algebra: Reduction of Linear Application

Covers the reduction of a linear application and finding corresponding reduced forms and bases.

Matrix Multiplication: Basics and Properties

Covers the basics of matrix multiplication, including properties and examples.

Diagonalization of Matrices

Explains the diagonalization of matrices, criteria, and significance of distinct eigenvalues.

Matrix Operations: LU Factorization & Subspace Vector

Covers LU factorization, determinants, subspaces, and matrix products.

Vector Spaces: Properties and Operations

Covers the properties and operations of vector spaces, including addition and scalar multiplication.

Diagonalization of Linear Transformations

Explains the diagonalization of linear transformations using eigenvectors and eigenvalues to form a diagonal matrix.