Course "Introduction to MatLab". MATLAB: tool of the future or an expensive toy Required level of training

Well " Introduction to MatLab" provides information about the capabilities of MatLab. During the course, students will learn to use the MaLab interpreter language to solve a wide range of problems.

Required level of training:

  • knowledge of programming basics;
  • skills in the Windows operating system.

Course program

1. Introduction

  • Scope of application of the MaLab system. Overview of MaLab socialized tools.

2. MATLAB Desktop Tools

  • Desk 3.
  • Main menu.
  • Project directory browser (Current Folders).
  • Command Window.
  • Window with the history of command calls (Command History).
  • Basic workspace window (Workspace Browser).
  • Editor.

3. Composition of the project directory

  • M-files.
  • SLX files.
  • FUR – files and utilities for working with them.
  • MAT files.

4 . Graphing tool

5. Language of the MatLab system

  • General characteristics of the MatLab language.
  • Variables and their types.
  • Arrays.
    • Methods for specifying an array.
    • Constructing arrays from arrays.
    • Subarrays.
    • Operations on arrays.
  • Structures.
  • Basic control structures.
  • M-functions and Anonymous functions.
  • Classes.
    • Class structure.
    • Mechanism of inheritance.
    • Properties section.
    • Methods section.
    • Events section.
    • Enumeration section.
    • Value class and pointer class (value classes, handle classes).
  • Events
  • Graphical data display tools
  • GUI Development Tools
  • eval string interpreter.
  • Symbolic calculations.

At the end of the course, a final certification is carried out in the form of a test or based on grades for practical work completed during the training process.

The MATLAB programming language is a high-level interpreted programming language that includes a wide range of functions, an integrated development environment, matrix-based data structures, and object-oriented capabilities written in other programming languages. The MatLab package was created by Math Works more than ten years ago. The work of hundreds of scientists and programmers is aimed at constantly expanding its capabilities and improving the underlying algorithms.

Today in our country more than 1000 enterprises use MATLAB tools to solve their problems. MATLAB is used in various fields of human activity: IoT, finance, medicine, space, automation, robotics, wireless systems and many others. etc. In a word, everything related to the ability to collect and visualize data, as well as forecasting.

Currently, MATLAB is a powerful and universal tool for solving problems, and specialists with MATLAB skills are in great demand in the labor market.

We invite you to MATLAB courses at the Interface Training Center to learn how to effectively work with MATLAB tools and quickly solve mathematical and economic problems.

Despite the fairly high popularity of the MATLAB language, most developers have difficulty understanding both its syntax and capabilities. The thing is that the language is directly related to a popular software product, the cost of which can reach amazing values. So, the main question is: is the Matlab language itself so good? And can it be useful for you?

Usage

Let's start not with a standard excursion into history and a discussion of the pros and cons of the language, but with the MATLAB/Simulink software environment - the only place where the hero of this text can be useful. Just imagine a graphic editor in which you can realize any of your ideas without having several years of experience and relevant education behind you. And having created a diagram of interaction between tools once, you will get a high-quality script for repeated use.

MATLAB is just such an editor in the data world. The scope of its application is infinitely wide: IoT, finance, medicine, space, automation, robotics, wireless systems and much, much more. In general, there are almost unlimited possibilities for collecting and visualizing data, as well as forecasting, but only if you have the opportunity to purchase the appropriate package.

As for the price, there is almost no upper limit, but the lower limit is around $99. To snatch such a powerful product for relatively little money, you need to be a university student. And of course you will get a rather limited product.

Features of the language

The MATLAB language is a tool that provides interaction between an operator (often not even a programmer) with all available capabilities for analyzing, collecting and presenting data. It has obvious pros and cons characteristic of a language living in a closed ecosystem.

Flaws:

    A slow and overloaded language with operators, commands, and functions, the main purpose of which is to improve visual perception.

    Narrowly focused. There is no other software platform where MATLAB is useful.

    High cost of software. If you are not a student, either get ready to empty your pockets or cross the line of the law. And even if you are a student, the price is decent.

    Low demand. Despite the great interest in MATLAB in almost every field, only a few actually and legally use it.

Advantages:

    The language is easy to learn and has a simple and understandable syntax.

    Huge opportunities. But this is rather an advantage of the product as a whole.

    Frequent updates, usually noticeable positive transformations occur at least a couple of times a year.

    The software environment allows you to convert it into “fast” code in C, C++.

The target audience

Of course, not everyone needs MATLAB. Despite its wide range of applications, it is difficult to imagine that the average application developer would need knowledge of this language. MATLAB is extremely useful in areas that require particularly robust data processing, such as autopilot systems in automobiles or aircraft avionics systems.

That is, if you are not much of a programmer, but one way or another your profession is related to the need for programmatic data processing, then a MATLAB/Simulink product with the appropriate language can greatly simplify your everyday tasks.

Literature

We conclude the review of the language, as always, with a list of educational literature. Of course, among them you will not find books exclusively on the language, but this will only make the perception of the language easier:

Do you have experience with MATLAB? And which?

For those who want to become a programmer - .

The course provides fundamental practical knowledge in the field of deep learning. Using various examples, the features of the operation and training of deep neural networks will be examined, and various implementations of architectures, both convolutional and recurrent deep neural networks, will be discussed.

Generating C/C++ code from MATLAB (MLEM) algorithms

The course provides practical skills in generating C code from MATLAB code. Describes how to prepare MATLAB code for code generation and how to generate optimal C code. The course shows an example of setting up interfaces and integrating generated C code into an external project.

Integration of C/C++ code into SIMULINK (SLEX)

The course covers various techniques for integrating code into Simulink models. The main emphasis is on the integration of C code and MATLAB code. Topics covered include C MEX S functions, MATLAB code, and connecting external C functions using the Legacy Code Tool in Simulink.

Team Development Organization (SLMB)

The course provides practical skills in model-based design as applied to team and enterprise development. Provides guidance on managing and collaborating with Simulink models when working on large-scale projects.

MATLAB for Aerospace Professionals (MLBE-O)

The hands-on course is designed for aerospace engineers to provide a comprehensive introduction to the MATLAB technical computing environment. Fundamentals of data analysis, visualization, modeling and programming in MATLAB are key topics of the course.

MATLAB for Automotive Professionals (MLBE-A)

The hands-on course is designed for automotive engineers to provide a comprehensive introduction to the MATLAB technical computing environment. Fundamentals of data analysis, visualization, modeling and programming in MATLAB are key topics of the course.

Systems and Algorithms Modeling (SLBE)

The course is designed for engineers who are new to modeling systems and algorithms. Emphasis is placed on the application of basic modeling techniques, model assembly verification, and tools for developing Simulink block diagrams.

Digital Signal Processing System Design (SLBE-G)

The course is intended for those DSP specialists who do not have professional experience in Simulink®. Based on the use of basic methods and tools for building models, skills will be given in developing models in the form of block diagrams for building digital signal processing systems.

Data processing and visualization in MATLAB (MLVI)

The course focuses on importing and preparing data for developing data analytics applications. The course will be useful to analysts and Data Scientists who need to automate the processing, analysis and visualization of heterogeneous data obtained from many sources.

Machine Learning with MATLAB (MLML)

The course focuses on data analysis and machine learning methods in MATLAB. Discusses unsupervised learning techniques for exploring and detecting features in large data sets and supervised learning techniques for building predictive models. Methods of visualization and evaluation of results will be shown using examples and exercises.

Deep Learning in MATLAB (MLDL)

The course provides fundamental practical knowledge in the field of deep learning. Using various examples, the features of the operation and training of deep neural networks are examined, and various implementations of architectures, both convolutional and recurrent deep neural networks, are discussed.

Signal Preprocessing and Extraction with MATLAB (MLSP)

This one-day course will show you how to use MATLAB, Signal Processing Toolbox, and Wavelet Toolbox to process timing signals and extract key features in the time and frequency domains. This course is designed for data scientists and engineers involved in signal (time series) analysis.

Programming in MATLAB (MLPR)

Hands-on experience using the features of the MATLAB language to write efficient, well-structured, and readable code. These concepts form the basis for creating applications, developing algorithms, and enhancing the capabilities of the products being developed. The course covers the details of optimizing code performance, as well as tools for writing and debugging code.

Integration of C/C++ code in MATLAB (MLEX)

The course focuses on the interaction of MATLAB and custom C code. Practical examples and exercises cover generating MEX files for integrating external C code into MATLAB applications and calling MATLAB code from applications written in C.

Object-Oriented Programming in MATLAB (MLCO)

Course participants will learn to use object-oriented programming to develop and support complex applications. In addition, a test-driven development approach to ensure software quality will be introduced.

Acceleration and parallelization of MATLAB code (MLAC)

The course will present various techniques for accelerating MATLAB code. You will learn to find and eliminate bottlenecks in code using memory allocation and vectorization techniques, compiling programs in MEX, and running code on multi-core CPUs and GPUs.

Creating GUIs with MATLAB (MLAP)

The course provides skills in creating interactive user interfaces for programs in MATLAB. You will learn about using custom controls such as buttons, sliders, graphics, and menus to create a robust and user-friendly interface for your MATLAB application.

Financial Analysis in MATLAB (MLFA)

The course is intended for professionals in the field of computational finance. It provides a comprehensive introduction to the technical computing environment MATLAB. Topics in data analysis, visualization, modeling, and programming are covered throughout the course, with an emphasis on practical applications for financial applications in problems such as time series analysis, Monte Carlo simulation, analysis, and portfolio management.

Credit Risk Management in MATLAB (MLCR)

The course provides a comprehensive introduction to credit risk modeling using MATLAB and computational finance tools. Useful for risk practitioners with MATLAB experience developing credit risk models using general modeling techniques and the Basel II/III Extended Internal Ratings approach.

Time Series Modeling in MATLAB (MLTS)

The course provides a thorough understanding of time series modeling using MATLAB. The training is intended for economists, analysts, and finance professionals with MATLAB experience developing time series models. The course is based on the standard Box-Jenkins procedure for developing time series models.

Market Risk Management in MATLAB (MLMR)

The course provides fundamental skills in managing market risk using MATLAB and financial instruments. The course is designed for risk analysts, risk managers, portfolio managers and other financial professionals with MATLAB experience who need to analyze, evaluate and manage market risks. The course uses market risk examples, although the techniques demonstrated are applicable to most risk areas, including liquidity, interest rate, and operational risk.

Systems and Algorithms Modeling (SLBE)

The course is designed for engineers who are new to modeling systems and algorithms. Emphasis is placed on the application of basic modeling techniques, model assembly verification, and tools for developing Simulink block diagrams.

Simulation of Systems and Algorithms for Automotive Enterprises (SLBE-A)

The course is designed for automotive engineers who are new to system modeling and algorithms. Emphasis is placed on the application of basic modeling techniques, model assembly verification, and tools for developing Simulink block diagrams.

Modeling Systems and Algorithms for Aerospace Enterprises (SLBE-O)

The course is designed for aerospace engineers who are new to system and algorithm modeling. Emphasis is placed on the application of basic modeling techniques, model assembly verification, and tools for developing Simulink block diagrams.

Development of state machines and control logic (SLSF)

This course examines the use of Stateflow to model control logic and state machines. The course is designed for Simulink users who are involved in modeling event-driven and high-level control systems. The course emphasizes the use of state machines and truth tables when developing in Simulink.

Modeling Queues and Discrete Event Systems (SLSE)

The practical course is devoted to discrete event modeling using the SimEvents tool. We consider the modeling of processes in systems that depend not on time, but on the occurrence of one or another event. Examples of such systems could be: a manufacturing process, a supply chain, a communication channel, a processor or software product architecture.

Powertrain Simulation and Calibration (SLMC)

The course emphasizes tools and techniques for experimental design, statistical modeling, and optimization techniques for calibrating modern powertrains in MATLAB and Simulink. The course is designed for engineers who are involved in calibration, testing, development of control algorithms for ECM and mathematical modeling of the power unit.

Development of robotic systems with ROS and GAZEBO in MATLAB (MLRO)

The training is intended for engineers involved in the development of motion algorithms for mobile robots based on the Robot Operating System (ROS) and the Gazebo simulator.

Semi-Life Modeling (SLRP)

The practical course is devoted to testing and debugging control algorithms in hard real time. The work with real-time machines is considered, as well as the capabilities of the Simulink Test tool, designed for formal testing of algorithms.

Development and prototyping of communication systems with SDR USRP (SLZR)

In the course you will learn to perform dynamic simulations of single- and multi-carrier digital communications systems in MATLAB®. As part of the course, we get acquainted with multi-antenna communication systems, turbo coding, and models of propagation channel imperfections. Components of LTE and IEEE 802.11 systems are used as examples. Students will build a radio-in-the-loop system using RTL-SDR or USRP® hardware platforms.

Design of the physical layer of communication systems of LTE and LTE ADVANCED (MLTE) standards

The course is aimed at studying the basic principles of building the physical layer of communication systems of the LTE and LTE-Advanced standards. By completing this course, students will learn how to generate LTE reference signals, as well as how to conduct end-to-end simulation of the passage of a signal from a transmitter to a receiver through a communication channel.

Digital Signal Processing System Design (SLBE-G)

The course is intended for those DSP specialists who do not have professional experience in Simulink®. Based on the use of basic methods and tools for building models, skills will be given in developing models in the form of block diagrams for building digital signal processing systems.

Simulation of Radio Frequency Path (SLRF)

Learn to use RF Blockset and RF Toolbox to model RF circuits in wireless communications systems. You will learn how to choose between two different paradigms for modeling RF signals: Equivalent Baseband and Circuit Envelope, and learn basic techniques for RF path simulation and simulation.

Communication systems design (SLCM)

Through practical examples, you will learn how to use Simulink products to design common communication systems. Emphasis is placed on end-to-end design and modeling of communication systems from transmitter to receiver using Simulink.

Creation of software components for the AUTOSAR architecture (SLAS)

The course focuses on AUTOSAR-compatible simulation and code generation using the Simulink code generator support package for AUTOSAR. In the context of model-based design, software development is considered using top-down and bottom-up methods. The course is intended for automotive software developers and systems engineers using Embedded Coder to automatically generate C/C++ code.

Automatic code generation for ZYNQ (SLZQ)

The practical course is aimed at studying the process of developing and configuring models in the Simulink environment and deploying them on the Xilinx® Zynq®-7000 platform. The course is designed for Simulink users who plan to generate, validate, and deploy embedded C/C++ code and HDL code using Embedded Coder and HDL Coder. The course uses the ZedBoard™ development board.

Static analysis of C/C++ code for embedded systems (PSBF)

This course discusses the use of Polyspace Bug Finder to detect algorithmic defects, improve software quality metrics, and ensure the reliability of the final product. This hands-on course is designed for engineers developing software or models for embedded systems.

Verification of C/C++ code with LDRA tools (LDRA)

The course aims to provide participants with a thorough understanding of advanced testing methodologies, as well as the requirements and limitations associated with developing applications to meet industry standards such as DO-178C and DO-278 in avionics, ISO 26262 in automotive, IEC 61508 in industrial safety and IEC 62304 in medical devices.

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