Lectures related to Numerical Analysis: Course Overview

Numerical Analysis: Algorithms and Concepts

Covers algorithms for mathematical problems, focusing on stability, precision, and critical result analysis.

Floating Point Numbers: Representation and Errors

Introduces floating point number representation, round-off errors, and their impact on numerical computations.

Covers the basics of Numerical Analysis, emphasizing the importance of numerical methods in solving mathematical problems with computers.

Forced Convection Correlations

Explores forced external convection correlations and the procedure for solving convection problems, including the comparison of velocity and thermal boundary layers.

Numerical Methods in Matlab

Covers derivation, integration, and simulation in Matlab, along with the representation of numbers and potential errors.

Floating Point Numbers: Representation and Errors

Explores floating point numbers, their representation, precision, and associated errors.

Numerical Methods for Boundary Value Problems

Covers numerical methods for solving boundary value problems using finite difference, FFT, and finite element methods.

Nonlinear Equations: Representation of Real Numbers

Discusses the representation of real numbers in various bases and the implications of floating-point arithmetic.

Floating Point Numbers: LU Decomposition and Errors

Explores floating point numbers, LU decomposition, errors, and matrix properties.

Floating Point Representation in Computers

Covers the representation of real numbers in a computer using floating point representation and the dangers of significant digit cancellation.

Scientific Computing in Neuroscience

Explores the history and tools of scientific computing in neuroscience, emphasizing the simulation of neurons and networks.

Scientific Computing with Python

Covers the basics of scientific computing with Python, focusing on programming, algorithms, and numerical methods.

Jupyter Notebooks for Numerical Analysis

Covers the transition to Python and Jupyter notebooks for Numerical Analysis at EPFL, including interactive exercises and graded tests.

Floating Point Numbers: LU Decomposition and Errors

Explores floating point numbers, LU decomposition, errors in numerical computations, and the impact of round-off errors.

Numerical Methods: Differential Equations

Covers the application of numerical methods to solve differential equations using MATLAB.

AI for Science

Explores fundamental principles in scientific research, the impact of computers, numerical algorithms, and deep learning in solving high-dimensional problems.

Numerical Methods in Chemistry

Covers the implementation of numerical methods in MATLAB for solving chemical problems.

Numerical Physics: Fourier Transforms and Linear Algebra

Covers advanced topics in computational physics, focusing on Fourier transforms and linear algebra.

Introduction to LabVIEW

Covers the basics of LabVIEW, including its importance, history, functions, and tools available.

Numerical Analysis: Introduction to Interpolation Techniques

Covers the basics of numerical analysis, focusing on interpolation methods and their applications in engineering.