# MATLAB Training Courses

MATLAB Statistical Software courses

## Client Testimonials

MATLAB Programming

Tomasz (the trainer) was knowledgeable and friendly and made the training very interesting. He helped me learnt a lot about a subject I was very new to.

Paul Cox - Network Rail

MATLAB Programming

## MATLAB Course Outlines

Code Name Duration Overview
matlabfincance Matlab for Finance 14 hours MATLAB integrates computation, visualization and programming in an easy to use environment. It offers Financial Toolbox, which includes the features needed to perform mathematical and statistical analysis of financial data, then display the results with presentation-quality graphics. This instructor-led training provides an introduction to MATLAB for finance. We dive into data analysis, visualization, modeling and programming by way of hands-on exercises and plentiful in-lab practice. By the end of this training, participants will have a thorough understanding of the powerful features included in MATLAB's Financial Toolbox and will have gained the necessary practice to apply them immediately for solving real-world problems. Audience      Financial professionals with previous experience with MATLAB Format of the course     Part lecture, part discussion, heavy hands-on practice Overview of the MATLAB Financial Toolbox Objective: Learn to apply the various features included in the MATLAB Financial Toolbox to perform quantitative analysis for the financial industry. Gain the knowledge and practice needed to efficiently develop real-world applications involving financial data. Asset Allocation and Portfolio Optimization Risk Analysis and Investment Performance Fixed-Income Analysis and Option Pricing Financial Time Series Analysis Regression and Estimation with Missing Data Technical Indicators and Financial Charts Monte Carlo Simulation of SDE Models Asset Allocation and Portfolio Optimization Objective: perform capital allocation, asset allocation, and risk assessment. Estimating asset return and total return moments from price or return data Computing portfolio-level statistics, such as mean, variance, value at risk (VaR), and conditional value at risk (CVaR) Performing constrained mean-variance portfolio optimization and analysis Examining the time evolution of efficient portfolio allocations Performing capital allocation Accounting for turnover and transaction costs in portfolio optimization problems Risk Analysis and Investment Performance Objective: Define and solve portfolio optimization problems. Specifying a portfolio name, the number of assets in an asset universe, and asset identifiers. Defining an initial portfolio allocation. Fixed-Income Analysis and Option Pricing Objective: Perform fixed-income analysis and option pricing. Analyzing cash flow Performing SIA-Compliant fixed-income security analysis Performing basic Black-Scholes, Black, and binomial option-pricing Financial Time Series Analysis Objective: analyze time series data in financial markets. Performing data math Transforming and analyzing data Technical analysis Charting and graphics Regression and Estimation with Missing Data Objective: Perform multivariate normal regression with or without missing data. Performing common regressions Estimating log-likelihood function and standard errors for hypothesis testing Completing calculations when data is missing Technical Indicators and Financial Charts Objective: Practice using performance metrics and specialized plots. Moving averages Oscillators, stochastics, indexes, and indicators Maximum drawdown and expected maximum drawdown Charts, including Bollinger bands, candlestick plots, and moving averages Monte Carlo Simulation of SDE Models Objective: Create simulations and apply SDE models Brownian Motion (BM) Geometric Brownian Motion (GBM) Constant Elasticity of Variance (CEV) Cox-Ingersoll-Ross (CIR) Hull-White/Vasicek (HWV) Heston Conclusion
matlabprog MATLAB Programming 14 hours This two-day course provides a comprehensive introduction to the MATLAB® technical computing environment. The course is intended for beginner users and those looking for a review. No prior programming experience or knowledge of MATLAB is assumed. Themes of data analysis, visualization, modeling, and programming are explored throughout the course. Working with the MATLAB user interface Entering commands and creating variables Analyzing vectors and matrices Working with data types Automating commands with scripts Writing programs with logic and flow control Writing functions Visualizing vector and matrix data Working with data files Importing data Organizing data Visualizing data
matlabml1 Introduction to Machine Learning with MATLAB 21 hours MATLAB Basics MATLAB More Advanced Features BP Neural Network RBF, GRNN and PNN Neural Networks SOM Neural Networks Support Vector Machine, SVM Extreme Learning Machine, ELM Decision Trees and Random Forests Genetic Algorithm, GA Particle Swarm Optimization, PSO Ant Colony Algorithm, ACA Simulated Annealing, SA Dimenationality Reduction and Feature Selection
ipmat1 Introduction to Image Processing using Matlab 28 hours This four day course provides image processing foundations using Matlab. You will practise how to change and enhance images and even extract patterns from the images. You will also learn how to build 2D filters and apply them on the images. Examples and exercises demonstrate the use of appropriate Matlab and Image Processing Toolbox functionality throughout the analysis process. Day 1: Loading images Dealing with RGB components of the image Saving the new images Gray scale images Binary images Masks Day 2: Analyzing images interactively Removing noise Aligning images and creating a panoramic scene Detecting lines and circles in an image Day 3: Image histogram Creating and applying 2D filters Segmenting object edges Segmenting objects based on their color and texture Day 4 Performing batch analysis over sets of images Segmenting objects based on their shape using morphological operations Measuring shape properties
smlk Simulink® for Automotive System Design 28 hours Objective: This training is meant for software Engineers who are working with MBD technology,the training will cover Modelling techniques for Automotive systems, Automotive standards ,Auto-code generation and Model test harness building and verification Audience: Software developper for automotive supplierFundamentals & Basics Using the MATLAB® environment Essential Mathematics for control systems using MATLAB® Graphics and Visualization Programming using MATLAB® GUI Programming using MATLAB®(optional) Introduction to Control systems and Mathematical Modeling using MATLAB® Control Theory using MATLAB® Introduction to systems modeling using SIMULINK® Simulink® internals (signals, systems, subsystems, simulation Parameters,…etc) Stateflow for automotive systems(Automotive Body Controller application) Introduction to MAAB( Mathworks® Automotive Advisory Board) Introduction to AUTOSAR AUTOSAR SWCs modeling using Simulink® Simulink Tool boxes for Automotive systems Hydraulic Cylinder Simulation Introduction to SimDrivelin (Clutch Models ,Gera Models)(Optional) Modeling ABS (Optional ) Modeling for Automatic Code Generation Model Verification Techniques