Lectures related to Matlab/Octave: Test and Time Management

Explores Matlab/Octave widgets, time management, and structures for efficient programming.

Introduces structures in MATLAB and GNU Octave, covering arrays, cell arrays, and maps.

Introduces MATLAB and GNU Octave, covering basic concepts, workspace, data types, matrices, installation, and practical usage.

Introduces Matlab basics, error handling, and billiards project concepts.

Covers logical arrays, 3D surface plotting, parametric curves, interpolation, and fitting in Matlab.

Covers the management of time in microcontrollers, focusing on handling inputs.

Introduces programming basics, debugging, and structured software implementation using powerful tools.

Covers exam preparation tips and strategies for effective time management during the upcoming exam.

Explores the Flipped Classroom Assignment, focusing on peer assessment, feedback, and learner mastery.

Delves into student projects at EPFL, emphasizing the importance of transversal skills for future success.

Provides guidance on mentor responsibilities, active listening, and supporting students effectively.

Covers the analysis of static (rigid) structures, including equilibrium and free-body diagrams.

Covers the transition from school to university, emphasizing self-organization and motivation.

Covers feedback on presentations, highlighting positives and areas for improvement from different teams.

Explores Matlab programming with scripts and functions, vectorization, and 2D graphics.

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

Explores the editing process for video lectures at EPFL, including setup, editing iterations, and use of project management software.

Explores control structures, error handling, and data structures in MATLAB programming, emphasizing code readability and practical examples.

Covers industrial project management, scope and time management, risk management, and product development basics.

Covers parametric signal models and practical Matlab applications for Markov chains and AutoRegressive processes.