Lectures related to Scientific Computing Essentials

Covers the basics of computer science, emphasizing programming and algorithms for data analysis and simulations.

Numerical Methods: Stopping Criteria, SciPy, and Matplotlib

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

Termination Analysis using Dependency Pairs

Explores automated termination analysis using dependency pairs, covering classical and modern techniques, annual competitions, and tools like AProVE.

NumPy Arrays and Graphical Representations: Introduction

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

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.

Strings in C: Initialization and Memory Management

Covers the representation and management of strings in C, highlighting key differences from other programming languages.

C++ Project Organization: Object-Oriented Programming

Introduces the C++ POP course objectives, focusing on project development and object-oriented programming techniques.

Introduction to Compiler Theory and Language Processing

Introduces compiler theory, language processing, and the essential concepts behind building compilers.

Nonlinear Equation Resolution: Introduction to Bisection Method

Introduces the bisection method for resolving nonlinear equations using numerical techniques and Python programming.

Variables and Types: Introduction to C Programming

Introduces the fundamentals of variables and types in C programming, covering declaration, initialization, and the differences from Java.

Vectorization in Python: Efficient Computation with Numpy

Covers vectorization in Python using Numpy for efficient scientific computing, emphasizing the benefits of avoiding for loops and demonstrating practical applications.

Introduction to Computational Science: Course Overview and Objectives

Introduces the Computational Science option at EPFL, detailing course objectives and programming fundamentals using Python.

Introduction to LabVIEW Programming

Introduces LabVIEW programming, covering memory management, data types, and parallel programming concepts, with hands-on demonstrations.

Python Programming: Data Structures and Functions

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

Introduction to Programming: Basics of Python

Covers the basics of programming in Python, focusing on practical skills and hands-on exercises.

Scientific Computing with Python

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

Pointers in C: Understanding Their Utility and Applications

Covers the definition and applications of pointers in C programming, emphasizing their role in memory management and generic programming.

Introduction to Programming in ICC Course (SV)

Covers the theoretical and practical aspects of programming in the ICC course.

Root Finding Methods: Bisection and Secant Techniques

Covers root-finding methods, focusing on the bisection and secant techniques, their implementations, and comparisons of their convergence rates.