Matlab training. MATLAB: tool of the future or expensive toy. Required level of training
Despite the rather high popularity of the MATLAB language, most developers can hardly imagine 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 good? And can it be useful for you.
Usage
Let's start not with a standard digression 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 once you have created a scheme of interaction between tools, you can 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, almost unlimited possibilities for collecting and visualizing data, as well as forecasting, but only if you can buy the appropriate package.
As for the price, there is almost no upper limit, but the lower one 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.
Language Features
The MATLAB language is a tool that provides the interaction of an operator (often not even a programmer) with all the available possibilities for analyzing, collecting and presenting data. It has the obvious pros and cons of a language that lives in a closed ecosystem.
Flaws:
Slow and overloaded with operators, commands, functions, the language, the main purpose of which is to improve visual perception.
Highly focused. There is no other software platform where MATLAB is useful.
Expensive software. If you are not a student - either get ready to empty your pockets or cross the border of the law. And even if the student - the price is decent.
Low demand. Despite the great interest in MATLAB in almost all areas, only a few actually and legally use it.
Advantages:
The language is easy to learn, has a simple and clear syntax.
Huge opportunities. But this is rather the advantage of the whole product as a whole.
Frequent updates, as a rule, noticeable positive transformations occur at least a couple of times a year.
The software environment allows you to convert it into a "fast" code in C, C++.
The target audience
Of course, not everyone needs MATLAB. Despite the widest scope, it is hard to imagine that an ordinary application developer would need knowledge of this language. MATLAB is extremely useful in areas that require special reliability in data processing, such as autopilot systems in cars or aircraft on-board electronic systems.
That is, if you are not a very programmer, but one way or another your profession is related to the need for programmatic data processing, then the MATLAB / Simulink product with the appropriate language can greatly simplify your everyday tasks.
Literature
We complete the review of the language, as always, with a list of educational literature. By itself, among them you will not find books exclusively on the language, but this will make the perception of the language only easier:
Do you have experience with MATLAB? And which?
For those who want to become a programmer - .
Hello dear visitors of our portal Video Teacher. We want to provide you with video lessons on the programming system in the MATLAB program.
MATLAB is a high-level language and interactive environment for programming, numerical calculations, and visualization of results. With MATLAB, you can analyze data, develop algorithms, create models and applications.
The MATLAB system is offered by developers (Math Works, Inc.) as a market leader, primarily in the military-industrial complex system, in the aerospace industry and in the automotive industry, a high-level programming language for technical computing with a large number of standard application packages. The MATLAB system has absorbed not only the advanced experience in the development and computer implementation of numerical methods accumulated over the past three decades, but also the entire experience of the development of mathematics in the entire history of mankind. About a million legally registered users are already using this system. It is willingly used in their scientific projects by leading universities and scientific centers of the world. The popularity of the system is facilitated by its powerful Simulink extension, which provides convenient and simple tools, including visual object-oriented programming, for modeling linear and nonlinear dynamic systems, as well as many other system extension packages.
The language, toolkit, and built-in math functions let you explore different approaches and solve faster than using spreadsheets or traditional programming languages such as C/C++ or Java.
MATLAB is widely used in areas such as:
- signal processing and communication,
- image and video processing,
- control systems,
- test and measurement automation,
- financial engineering,
- computational biology, etc.
Watch video tutorials that will teach you how to work with MATLAB. These video tutorials are ideal for beginners who want to learn the basic skills of working with an application package that serves to solve various mathematical problems and technical calculations. Learn effectively and interestingly with us! You can find more information on MATLAB on the website
Well " Introduction to MatLab" provides information about the capabilities of MatLab. In the course, students will learn how to use the MaLab interpreter language to solve a wide range of problems.
Required level of training:
- knowledge of the basics of programming;
- skills in operating system Windows.
Course program
1. Introduction
- Scope of the MaLab system. An overview of socialized MaLab tools.
2. MATLAB Desktop Tools
- Desktop 3.
- Main menu
- Project Directory Browser (Current Folders).
- Command window (Command Windows).
- Window with Command History.
- Base 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 . Graph builder
5. Language of the MatLab system
- General characteristics of the MatLab language.
- Variables and their types.
- Arrays.
- Ways to set an array.
- Constructing arrays from arrays.
- Subarrays.
- Operations on arrays.
- structures.
- Basic control structures.
- M-functions and Anonymous functions.
- Classes.
- class structure.
- inheritance mechanism.
- Properties section.
- Methods section.
- Events section.
- Enumeration section.
- Value class and pointer class (value classes, handle classes).
- Events
- Graphical means of displaying data
- 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 on the basis of marks for practical work performed during the course of study.
The MATLAB programming language is a high-level interpreted programming language that includes a wide range of functions, an integrated development environment based on matrix data structures, object-oriented features written in other programming languages. MatLab package was created by Math Works over 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 areas of human activity: IoT, finance, medicine, space, automation, robotics, wireless systems, and more. etc. In a word, everything related to the possibility of collecting and visualizing data, as well as forecasting.
Currently, MATLAB is a powerful and versatile tool for solving problems, and specialists with the skills to work with MATLAB are in great demand in the labor market.
We invite you to take MATLAB courses at the Interface Training Center to learn how to work effectively with MATLAB tools and quickly solve mathematical and economic problems.
The course provides fundamental practical knowledge in the field of deep learning. Using various examples, we will understand the features of the operation and training of deep neural networks, as well as discuss various implementations of architectures, both convolutional and recurrent deep neural networks.
C/C++ code generation from MATLAB (MLEM) algorithms
The course provides practical skills in generating C code from MATLAB code. Explains how to prepare MATLAB code for code generation and how to perform optimal C code generation. The course shows an example of setting up interfaces and integrating the generated C code into an external project.
Integration of C/C++ code in SIMULINK (SLEX)
The course covers various methods for integrating code into Simulink models. The main focus 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 corporate development. Guides are provided for managing and collaborating with Simulink models when working on large scale projects.
MATLAB for Aerospace Professionals (MLBE-O)
The practical course is designed for aerospace engineers to provide a comprehensive introduction to the MATLAB technical computing environment. The fundamentals of data analysis, visualization, modeling and programming in MATLAB are the key topics of the course.
MATLAB for Automotive Professionals (MLBE-A)
The practical course is designed for automotive industry engineers to provide a comprehensive introduction to the MATLAB technical computing environment. The fundamentals of data analysis, visualization, modeling and programming in MATLAB are the key topics of the course.
Systems and Algorithms Modeling (SLBE)
The course is intended for engineers who are new to modeling systems and algorithms. Emphasis is placed on the application of basic modeling techniques, checking the correctness of the assembly of models and tools for developing Simulink block diagrams.
Digital Signal Processing 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 to develop 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 analysis applications. The course will be useful for 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. Unsupervised learning techniques for exploring and discovering features in large data sets and supervised learning for building predictive models are considered. Examples and exercises will show how to visualize and evaluate results.
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 analyzed, and various implementations of architectures, both convolutional and recurrent deep neural networks, are discussed.
Preprocessing and Extracting Signal Properties with MATLAB (MLSP)
This one-day course will show you how to use MATLAB, Signal Processing Toolbox, and Wavelet Toolbox to process time signals and extract key features in the time and frequency domains. This course is intended for data scientists and engineers involved in signal (time series) analysis.
Programming in MATLAB (MLPR)
Hands-on experience using MATLAB language features to write efficient, well-structured, and readable code. These concepts form the basis for creating applications, developing algorithms, and extending the capabilities of developed products. The course covers the details of code performance optimization, 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 the generation of 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 how to use object-oriented programming to develop and maintain complex applications. In addition, a test-driven development approach to ensure software quality will be presented.
Acceleration and parallelization of MATLAB code (MLAC)
The course will introduce various techniques for speeding up MATLAB code. You will learn how to find and eliminate bottlenecks in code using memory allocation and vectorization techniques, compiling programs in MEX, running code on multi-core CPUs and GPUs.
Building 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, graphs, and menus to create a robust and user-friendly interface for your MATLAB application.
Financial Analysis in MATLAB (MLFA)
The course is intended for specialists in the field of computational finance. It gives a comprehensive introduction to the MATLAB technical computing environment. Throughout the course, the topics of data analysis, visualization, modeling and programming are covered with an emphasis on practical applications for financial applications in solving problems such as time series analysis, Monte Carlo modeling, 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 experience in MATLAB developing credit risk models using common modeling techniques and the Basel II/III extended internal ratings approach.
Time Series Modeling in MATLAB (MLTS)
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Market Risk Management in MATLAB (MLMR)
The course provides fundamental market risk management skills using MATLAB and financial instruments. The course is intended for risk analysts, risk managers, portfolio managers and other financial professionals with experience in MATLAB who need to analyze, evaluate and manage market risks. The course uses examples of market risk, although the methods demonstrated are applicable to most risk areas, including liquidity, interest rate, and operational risk.
Systems and Algorithms Modeling (SLBE)
The course is intended for engineers who are new to modeling systems and algorithms. Emphasis is placed on the application of basic modeling techniques, checking the correctness of the assembly of models and tools for developing Simulink block diagrams.
Systems and Algorithms Modeling for the Automotive Industry (SLBE-A)
The course is intended for automotive engineers who are new to systems and algorithm modeling. Emphasis is placed on the application of basic modeling methods, checking the correctness of the assembly of models and tools for developing Simulink block diagrams.
System and Algorithm Modeling for Aerospace Enterprises (SLBE-O)
The course is designed for aerospace engineers who are new to systems and algorithm modeling. Emphasis is placed on the application of basic modeling methods, checking the correctness of the assembly of models and tools for developing Simulink block diagrams.
Finite State Machine and Control Logic Design (SLSF)
This course explores the use of Stateflow to model control logic and state machines. The course is designed for Simulink users who are modeling event and high-level control systems. The course focuses on 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. The modeling of processes in systems that depend not on time, but on the occurrence of an event is considered. Examples of such systems can be: a manufacturing process, a supply chain, a communication channel, a processor or software product architecture.
Powertrain Modeling and Calibration (SLMC)
The course focuses on tools and techniques for designing experiments, statistical modeling, and optimization methods 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 the 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-realistic simulation (SLRP)
The practical course is dedicated to testing and debugging control algorithms in hard real time. Work with real-time machines is considered, as well as the possibilities of the Simulink Test tool, designed for formal testing of algorithms.
Development and prototyping of communication systems with SDR USRP (SLZR)
In this course, you will learn how to conduct dynamic simulation digital systems links to one or more carriers in MATLAB®. As part of the course, we get acquainted with multi-antenna communication systems, turbo coding, propagation channel imperfection models. Components of LTE and IEEE 802.11 systems are used as examples. Students will assemble a radio-in-loop system using RTL-SDR or USRP® hardware platforms.
Physical layer design for LTE and LTE ADVANCED (MLTE) communication systems
The course is aimed at studying the basic principles of building the physical layer of communication systems of the LTE and LTE-Advanced standards. After completing this course, students will learn how to generate reference LTE signals, as well as how to conduct an end-to-end simulation of the signal from a transmitter to a receiver through a communication channel.
Digital Signal Processing 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 to develop models in the form of block diagrams for building digital signal processing systems.
Radio Frequency Path Modeling (SLRF)
Learn how to use RF Blockset and RF Toolbox to model RF circuits in wireless communication systems. You will learn how to choose between two different paradigms for RF signal modeling: Equivalent Baseband and Circuit Envelope, as well as learn the basic techniques for simulating and modeling an RF path.
Communication Systems Engineering (SLCM)
Use practical examples to learn how to use Simulink products to design common communication systems. Particular attention is paid to the end-to-end design and modeling of communication systems from transmitter to receiver using Simulink.
Creation of software components for AUTOSAR architecture (SLAS)
The course focuses on AUTOSAR-compatible modeling 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 software developers in the automotive industry and systems engineers who use Embedded Coder to automatically generate C/C++ code.
Automatic code generation for ZYNQ (SLZQ)
The practical course is aimed at learning the process of developing and configuring models in the Simulink environment and deploying them on the Xilinx® Zynq®-7000 platform. The course is intended for Simulink users who plan to generate, validate, and deploy Embedded C/C++ 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 how to use the Polyspace Bug Finder to find algorithmic defects, improve software quality metrics, and ensure end product reliability. This hands-on course is designed for engineers developing software or models for embedded systems.
C/C++ code verification with LDRA tools (LDRA)
The course aims to provide participants with a thorough understanding of advanced testing methodologies as well as the requirements and constraints associated with developing applications to comply with 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.
