This Specialization aims to take learners with little to no programming experience to being able to create MATLAB programs that solve real-world problems in engineering and the sciences. The focus is on computer programming in general, but the numerous language features that make MATLAB uniquely suited to engineering and scientific computing are also covered in depth. Topics presented range from basic programming concepts in the first course, through more advanced techniques including recursion, program efficiency, Object Oriented Programming, graphical user interfaces in the second course, to data and image analysis, data visualization and machine learning in the third course.
Course 1: Introduction to Programming with MATLAB - Offered by Vanderbilt University. This course teaches computer programming to those with little to no previous experience. It uses the ... Enroll for free.
Course 2: Mastering Programming with MATLAB - Offered by Vanderbilt University. The course builds on the foundation laid by the first course of the Specialization called “Introduction to ... Enroll for free.
Course 3: Introduction to Data, Signal, and Image Analysis with MATLAB - Offered by Vanderbilt University. Welcome to Introduction to Data, Signal, and Image Analysis with MATLAB! MATLAB is an extremely ... Enroll for free.
This course teaches computer programming to those with little to no previous experience. It uses the programming system and language called MATLAB to do so because it is easy to learn, versatile and very useful for engineers and other professionals. MATLAB is a special-purpose language that is an excellent choice for writing moderate-size programs that solve problems involving the manipulation of numbers. The design of the language makes it possible to write a powerful program in a few lines. The problems may be relatively complex, while the MATLAB programs that solve them are relatively simple: relative, that is, to the equivalent program written in a general-purpose language, such as C++ or Java. As a result, MATLAB is being used in a wide variety of domains from the natural sciences, through all disciplines of engineering, to finance, and beyond, and it is heavily used in industry. Hence, a solid background in MATLAB is an indispensable skill in today’s job market.
Nevertheless, this course is not a MATLAB tutorial. It is an introductory programming course that uses MATLAB to illustrate general concepts in computer science and programming. Students who successfully complete this course will become familiar with general concepts in computer science, gain an understanding of the general concepts of programming, and obtain a solid foundation in the use of MATLAB.
Students taking the course will get a MATLAB Online license free of charge for the duration of the course. The students are encouraged to consult the eBook that this course is based on. More information about these resources can be found on the Resources menu on the right.
The course builds on the foundation laid by the first course of the Specialization called “Introduction to Programming with MATLAB.” It covers more advanced programming concepts such as recursion, vectorization, function handles, algorithm efficiency and others. At the same time, it presents many features that make MATLAB a powerful programming environment for engineering and scientific computing, such as its support for object oriented programming, the new user interface design environment and Live Scripts.
By the end of this course, you will be familiar with more advanced computer programming concepts, able to write more efficient code, and able to create object oriented MATLAB applications with graphical user interfaces.
Welcome to Introduction to Data, Signal, and Image Analysis with MATLAB!
MATLAB is an extremely versatile programming language for data, signal, and image analysis tasks. This course provides an introduction on how to use MATLAB for data, signal, and image analysis. After completing the course, learners will understand how machine learning methods can be used in MATLAB for data classification and prediction; how to perform data visualization, including data visualization for high dimensional datasets; how to perform image processing and analysis methods, including image filtering and image segmentation; and how to perform common signal analysis tasks, including filter design and frequency analysis.