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MAT LAB Detail Syllabus
(Duration 75 Hrs)
Start Date
(21st feb)
Course Fee
(Rs:5000/- Only)

MAT LAB Syllabus Details


• Key features
• Architecture
• Installation of MATLAB
• Use of MATLAB

Module 2:MATLAB Simulation Software

• Introduction to MATLAB Software
• MATLAB windows
• Command Window
• Editor Window
• Workspace
• Command History
• Current directory
• Working with the MATLAB user interface

Module 3:Working with data files and data types

Writing Script Files
• Data types
 Numeric
 String
• Data type conversion
 Numeric to String
 String to Numeric

Module 4:Operators & Special characters

• Arithmetic operators
• Bit-Wise Operators
• Relational Operators
• Logical Operators
• Set operations
• Special chracters

Module 5:Complex Numbers & Trigonometric functions

To work with complex numbers and trigonometric functions in MATLAB

Module 6:Matrices and Arrays

• Array Initializations
• About Matrices
• Generating Matrices
• Matrix Sum, transpose, diagonal, inverse
• Matrix Multiplication, division
• The magic Function
• Matrix and Array Operations
• Matrices and Magic Squares
• Generating Arrays Using MATLAB Function

Module 7:Types of Arrays

• Multidimensional Arrays
• Extending Multidimensional Arrays
• Structures
• Cell Arrays

Module 8:Loops and Conditional Statements

• Control Flow
• Conditional Control — if, else, switch
• Loop Control — for, while, continue, break
• Program Termination — return

Module 9:Functions

• Writing user defined functions
• Built in Function
• Function calling
• Return Value
• Types of Functions
• Global Variables

Module 10:Plots

• Plotting vector and matrix data
• Plot labelling, curve labelling, legend and colour bar editing
Plot types
2-D Plots
• Basic Plotting Functions
• Creating a Plot
• Plotting Multiple Data Sets in One Graph
• Specifying Line Styles and Colors
• Graphing Imaginary and Complex Data
• Figure Windows
• Displaying Multiple Plots in One Figure
• Controlling the Axes
3-D Plots
• Creating Mesh and Surface
• About Mesh and Surface Visualizing
• Subplots
Examples: Deal with complex plot

Module 11:M-Files

• The MATLAB Editor
• Script M-files
• The MATLAB path
• Function M-files
• Sub-functions and nested functions
• Debugging
Best script file writing tactics

Module 12:MATLAB Programming

• Automating commands with scripts
• Writing programs with logic and flow control
• Writing functions
• Control statement Programming
• Conditional Statement Programming
• Examples

Module 13:Symbolic Math in MATLAB

• The MATLAB Editor
• Script M-files
• The MATLAB path
• Function M-files
• Sub-functions and nested functions
• Debugging

Module 14:Publishing Report

• Create the cell script
• Execute the cell script
• Publish the Script in HTML
• Publish the script in LATEX
• Report Generation

Module 15:Different application in MATLAB

• Statistical parameter estimations
• DSP applications
• Image Processing applications
• Control System applications
• Robotics Application
• Financial Application
• Time-Series Application

Module 16:Graphical User Interface Design

• Introduction Of GUI
• GUI Function Property
• GUI Component Design
• GUI Container
• Writing the code of GUI Callback
• Dialog Box
• Menu Designing
• Applications

Module 17:MATLAB Simulink

• Introduction Of Simulink
• Simulink Environment & Interface
• Study of Library
• Circuit Oriented Design
• Equation Oriented Design
• Connectivity
• Model
• Subsystem Design
• Connect Call back to subsystem
• Application

Module 18:MATLAB for Financial Applications Computational Finance

• Time-series analysis
• Fixed-income security valuation
• Portfolio management
• Options and derivatives
• Monte Carlo simulation
• Representing dates and durations
• Performing calculations with dates and durations
• Extracting numeric components of dates and durations
• Applying mathematical operations to variables
• Performing calculations efficiently using numerical operations
• Calculating descriptive data statistics

Module 19:Optimization Techniques in MATLAB

• Specifying objective functions
• Specifying constraints
• Choosing solvers and algorithms
• Evaluating results and improving performance
• Using global optimization methods
• Identifying the problem components
• Running an optimization using Optimization Tool
• Applying the optimization process
• Using optimization functions
• Using an objective function file
• Specifying objective functions with function handles
• Passing extra data to objective functions
Specifying Constraints
• Identifying different types of constraints
• Defining bounds
• Defining linear constraints
• Defining nonlinear constraints
Global Optimization
• Finding the global minimum
• Using genetic algorithms to solve discrete problems

Module 20:Statistical Methods in MATLAB

Importing and Organizing Data
• Data types
• Dataset arrays
• Merging data
• Categorical data
• Missing data
Exploring Data
• Central tendency
• Spread
• Shape
• Correlations
• Grouped data
• Probability distributions
• Distribution parameters
• Comparing and fitting distributions
• Nonparametric fitting
• Distribution objects
Hypothesis Tests
• Tests for normal distributions
• Tests for non-normal distributions
ANOVA Testing
• One-way ANOVA
• N-way ANOVA
• Nonnormal ANOVA
• Categorical correlations Regression
• Linear regression models
• Fitting linear models to data
• Evaluating the fit
• Adjusting the model
• Logistic and generalized linear regression
• Nonlinear regression

Module 21:Machine Learning with MATLAB

Importing and Organizing Data
• Data types
• Tables
• Categorical data
• Data preparation
Finding Natural Patterns in Data
• Unsupervised learning
• Self-Organizing Maps
• Clustering methods
• Cluster evaluation and interpretation
Building a Predictive Model
• Supervised learning
• Training and validation
• Classification methods
• Neural Networks
• Wilcoxon Rank based Learning

Module 22:Risk Management with MATLAB

• Creating market and sector baselines
• Computing risk metrics for a given portfolio
• Computing portfolio betas
• Computing relative portfolio risk
• Creating and simulating market risk models
• Identifying and modeling serial autocorrelation and GARCH effects
• Risk-oriented GARCH time-series models
• Extreme-value theory and copulas
• Filtered historical bootstrapping
• Estimating transition probabilities from credit ratings migration data
• Determining credit quality thresholds
• Forecasting corporate default rates
• Pricing fixed-income securities

Module 23:Signal Processing in MATLAB

• Introduction to DSP
• Creating discrete signals
• Sampling and resampling
• Visualizing signals
• Modeling noise
• Performing resampling, modulation, and correlation Spectral Analysis
• Windowing and zero padding
• Power spectral density estimation
• Time-varying spectra
• Using a spectrum analyzer in MATLAB Linear Time Invariant Systems
• LTI system representations
• z-transform
• Frequency and impulse response
• Visualizing filter properties
• Applying filters to finite and streaming signals
Filter Design
• Interactive filter design
• Common filter design functions
• Filter design with filter specification objects
• Reducing filter delay
• Frequency-domain filtering
The Signal Analysis App
• Browse signals and make simple measurements
• Perform interactive spectral analysis
• Design and apply filters to signals interactively
Multirate Filters
• Downsampling and upsampling
• Noble identities and polyphase FIR structures
• Polyphase decimators and interpolators
• Design multistage and interpolated FIR filters Adaptive Filter Design
• Basics of adaptive filtering
• Perform system identification
• Perform noise cancellation
• Improve adaptive filter efficiency

Module 24:Principle of Soft Computing (Tool Box Application)

Introduction to Soft Computing
Fuzzy Logic System
• Mamdani Fuzzy System
• Takagi-Sugeno-Kang
Artificial Neural Network
• MLP (Multi-layer Perceptron)
• RBF (Radial basis Function)
• RNN (Recurrent Neural Network)
• Hoffman Neural Network
Function Approximation
System Identification
Evolutionary Algorithm
• Genetic Algorithm

Module 25:Image Processing with MATLAB

Importing and Visualizing Images
• Importing and displaying images
• Converting between image types
• Exporting images
• Importing and playing video files
Interactive Exploration of Images • Obtaining pixel intensity values
• Extracting a region of interest
• Computing pixel statistics on a region of interest
• Measuring object sizes
• Creating a custom interactive tool Preprocessing Images
• Adjusting image contrast
• Reducing noise in an image
• Using sliding neighborhood operations
• Using block processing operations
Spatial Transformation and Image Registration
• Create a panoramic scene by stitching images.
• Geometric transformations
• Image registration using point mapping
• Creating a panoramic scene
Edge and Line Detection • Segmenting object edges
• Detecting straight lines
• Performing batch analysis over sets of images
• Detecting circular objects Color and Texture Segmentation
• Color space transformation
• Color segmentation
• Texture segmentation
• Texture based image classification Feature Extraction
• Counting objects
• Measuring shape properties
• Using morphological operations
• Performing watershed segmentation

Module 26:Interfacing MATLAB with C Code

MEX-File Overview
• Introduction to MEX-files
• Applications of MEX-files
• Components of a MEX-file
• Setting up MATLAB to compile MEX-files
• Building and running a MEX-file
MEX-Files with Inputs and Outputs • Data flow in MEX-files
• MATLAB data
• The mxArray class
• Working with pointers
• Working with mxArray API functions
• Working with strings
• When to use MEX-files
• Handling data
MEX-File Interface Considerations • Displaying diagnostic messages
• Memory allocation and deallocation
• Preventing memory leaks
• Working with input and output memory
• Debugging MEX-files Calling MATLAB from C Code
• Data flow in MATLAB engine applications
• Calling the MATLAB engine
• Compiling and running MATLAB engine applications

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