Lectures related to Introduction to Computational Science: Course Overview and Objectives

Numerical Analysis: Introduction to Interpolation Techniques

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

Introduces a programming course for engineers, emphasizing the importance of mastering multiple languages for future projects.

Programming for Engineers: Motivation and Exercises

Covers the motivation behind controlling a babyfoot game and presents exercises related to programming in C.

Scientific Computing Essentials

Covers algorithmic thinking, Python programming, numerical methods, and essential computing concepts for scientific computing.

Taylor Series and Secant Method: Numerical Analysis Techniques

Discusses the Taylor series and secant method, focusing on their applications in numerical analysis and root-finding techniques.

Programming for Engineers

Covers programming concepts, memory management, debugging, and advanced MATLAB functionalities.

Computational Science and Engineering: Master Program Overview

Outlines the Master in Computational Science and Engineering program at EPFL, detailing its structure, projects, and career opportunities for graduates.

Quiz LV

Covers examples related to racing programming for engineers, focusing on values of variables, debugging, and shift registers.

Programming for Engineers

Covers programming concepts using MATLAB, C, and LabVIEW for engineering projects, including a billiards game analysis.

Python Programming: Lists and Functions Overview

Introduces Python programming concepts, focusing on lists, functions, and their applications in problem-solving.

Python Programming: Data Structures and Functions

Covers advanced Python programming concepts, including data structures and functions.

Programming for Engineers: Overview and Interactive Quiz

Covers programming concepts for engineers, including interactive quizzes and practical applications to enhance learning and engagement.

Numerical Methods: Stopping Criteria, SciPy, and Matplotlib

Discusses numerical methods, focusing on stopping criteria, SciPy for optimization, and data visualization with Matplotlib.

LabVIEW: Display and DAQ

Covers LabVIEW programming for data acquisition and display, including LabVIEW sound and visualization.

Introduction to NumPy: Basics of Scientific Computing

Introduces NumPy, focusing on array creation, manipulation, and its advantages for scientific computing.

Numerical Methods: Bisection and Multidimensional Arrays

Discusses the bisection method for solving nonlinear equations and its implementation using Python with NumPy and Matplotlib.

Programming for Engineers

Covers programming basics for engineers, emphasizing MATLAB, C, and LabVIEW tools for project development.

Python Programming: Dictionaries and Classes

Introduces Python programming concepts, focusing on dictionaries and classes, including their definitions, usage, and practical examples.

Introduction to NumPy and Matplotlib for Scientific Computing

Introduces NumPy and Matplotlib, essential tools for scientific computing and data visualization in Python.

NumPy Arrays and Graphical Representations: Introduction

Covers NumPy arrays and their graphical representations using Matplotlib, focusing on array creation, manipulation, and visualization techniques.