Objective: Become familiar with the main features of the MATLAB integrated design environment and its user interfaces. Interactively create customized visualizations that can be used for financial reporting.Importing data from filesSaving and loading variablesVisualizing data interactivelyExploring and customizing graphicsSharing graphical results
Variables and Commands
Objective: Enter MATLAB commands, with an emphasis on creating and accessing numeric and text data. Collect MATLAB commands into code files for reproduction and automation. Learn how to perform tasks such as data import, analysis, and report generation.Entering commandsCreating numeric and text variablesFinding help and documentationImporting data programmaticallyAccessing and modifying values in variablesCreating and running scripts
Visualizing Results
Objective: Create informative visualizations of numeric and time-based data. Enhance the appearance of charts by customizing graphics and annotations.Visualizing dataCustomizing graphics optionsWorking with individual graphics componentsAnnotationConverting between numbers and text
Data Analysis
Objective: Perform mathematical and statistical calculations on numerical data. Use MATLAB syntax to perform preprocessing and analysis tasks on multiple price series with single commands.Performing calculations on dataInterpreting matrix dataUsing matrices for analysis
Dates and Times
Objective: Use variables to represent and manipulate dates and time durations. Extract components of dates and durations as numeric variables.Representing dates and durationsPerforming calculations with dates and durationsExtracting numeric components of dates and durationsPlotting with dates
Working with Tabular Data
Objective: Import data as a MATLAB table. Work with tabular financial datasets that include mixed data types.Storing data in tablesExtracting data from tablesModifying tablesTable operationsExporting data from tables
Conditional Data Selection
Objective: Analyze subsets of data that satisfy given criteria. Perform fast data extraction and manipulation using logical variables.Defining logical conditions using logical operatorsExtracting and filtering data by indexing with a logical variableIdentifying and counting subsets of dataManaging discrete variables using categorical arrays
Programming Flow Control
Objective: Create flexible code that can interact with the user, make decisions, and adapt to different situations. Automate tasks using programming constructs.Managing command-driven and graphical interaction with a userControlling program flow using conditional programming constructsPerforming iterative tasks using loops
Working with Missing Data
Objective: Perform statistical calculations on data with missing values. Identify, remove, and replace missing values in a data set.Locating missing valuesIgnoring, removing, and replacing missing values
Customizing Graphics
Objective: Create charts comprising multiple graphics components. Use color, text, and data manipulation techniques to produce eye-catching visualizations.Working with the MATLAB graphics hierarchyAccessing and modifying individual graphics componentsManaging graphical tables
Fitting Models to Empirical Data
Objective: Preprocess data prior to model fitting. Fit probability distributions and linear models to data. Generate random numbers from a theoretical or fitted distribution.Fitting linear regression modelsFitting probability distributionsSimulating from distribution fits
Increasing Automation with Functions
Objective: Increase automation by encapsulating modular tasks as user-defined functions. Understand how MATLAB resolves references to files and variables. Explore MATLAB tools for debugging code.Creating and calling functionsManaging data in workspacesWriting plain text code filesManaging the MATLAB pathDebugging code filesSimplifying interfaces using structures
Objective: Read text files that contain a mixture of data types, delimiters, and headers.Import a mixture of data types from arbitrarily formatted text filesImport only required columns of data from a text fileImport and merge data from multiple files
Processing Data
Objective: Process raw imported data by extracting, manipulating, aggregating, and counting portions of data.Process data with missing elementsCreate and modify categorical arraysAggregate, bin, and count groups of data
Customizing Visualizations
Objective: Annotate and modify standard plots to produce informative customized graphics.Determine properties of graphics objects and their associated valuesLocate and manipulate graphics objectsCustomize plots by modifying properties of graphics objects
Working with Irregular Data
Objective: Import and visualize scattered data from text files with irregular formatting.Parse text files to determine formattingImport data from separate sections of a text fileExtract data from container variablesInterpolate irregularly spaced three-dimensional dataVisualize three-dimensional data in two and three dimensions
Objective: Understand the basic structure and process of solving optimization problems effectively. Use interactive tools to define and solve optimization problems.Identifying the problem componentsRunning an optimization using the Live Editor Optimization TaskApplying the optimization processUsing optimization functions
Specifying Objective Functions and Constraints
Objective: Write an optimization problem. Use problem-based workflow to arrive at a solution.Using the problem-based workflowSpecifying objective functions and constraintsIdentifying different types of constraints
Choosing a Solver and Improving Performance
Objective: Select an appropriate solver and algorithm by considering the type of optimization problem to be solved. Interpret the output from the solver and diagnose the progress of an optimization.Classifying the objectiveChoosing a solver and algorithmExamining and interpreting the resultProviding derivative information
Global and Multiobjective Optimization
Objective: Use Global Optimization Toolbox functionality to solve problems where classical algorithms fail or work inefficiently. Solve problems with many objectives.Finding the global minimumUsing genetic algorithms, direct search methods and surrogate optimizationUse multiobjective solvers
Objective: Bring data into MATLAB and organize it for analysis, including normalizing data and removing observations with missing values.Data typesTablesCategorical dataData preparation
Finding Natural Patterns in Data
Objective: Use unsupervised learning techniques to group observations based on a set of explanatory variables and discover natural patterns in a data set.Unsupervised learningClustering methodsCluster evaluation and interpretation
Building Classification Models
Objective: Use supervised learning techniques to perform predictive modeling for classification problems. Evaluate the accuracy of a predictive model.Supervised learningTraining and validationClassification methods
Improving Predictive Models
Objective: Reduce the dimensionality of a data set. Improve and simplify machine learning models.Cross validationHyperparameter optimizationFeature transformationFeature selectionEnsemble learning
Building Regression Models
Objective: Use supervised learning techniques to perform predictive modeling for continuous response variables.Parametric regression methodsNonparametric regression methodsEvaluation of regression models
Creating Neural Networks
Objective: Create and train neural networks for clustering and predictive modeling. Adjust network architecture to improve performance.Clustering with Self-Organizing MapsClassification with feed-forward networksRegression with feed-forward networks
Objective: Perform image classification using pretrained networks. Use transfer learning to train customized classification networks.Pretrained networksImage datastoresTransfer learningNetwork evaluation
Interpreting Network Behavior
Objective: Gain insight into how a network is operating by visualizing image data as it passes through the network. Apply this technique to different kinds of images.ActivationsFeature extraction for machine learning
Creating Networks
Objective: Build convolutional networks from scratch. Understand how information is passed between network layers and how different types of layers work.Training from scratchNeural networksConvolution layers and filters
Training a Network
Objective: Understand how training algorithms work. Set training options to monitor and control training.Network trainingTraining progress plotsValidation
Improving Network Performance
Objective: Choose and implement modifications to training algorithm options, network architecture, or training data to improve network performance.Training optionsDirected acyclic graphsAugmented datastores
Performing Image Regression
Objective: Create convolutional networks that can predict continuous numeric responses.Transfer learning for regressionEvaluation metrics for regression networks
Using Deep Learning for Computer Vision
Objective: Train networks to locate and label specific objects within images.Image application workflowObject detection
Classifying Sequence Data
Objective: Build and train networks to perform classification on ordered sequences of data, such as time series or sensor data.Long short-term memory networksSequence classificationSequence preprocessingCategorical sequences
Generating Sequences of Output
Objective: Use recurrent networks to create sequences of predictions.Sequence to sequence classificationSequence forecasting
Objective: Import and visualize different image types in MATLAB. Manipulate images for streamlining subsequent analysis steps.Importing, inspecting, and displaying imagesConverting between image typesVisualizing results of processingExporting images
Preprocessing Images
Objective: Enhance images for analysis by using common preprocessing techniques such as contrast adjustment and noise filtering.Adjusting contrastReducing noise with spatial filteringEqualizing inhomogeneous backgroundProcessing images in distinct blocksMeasuring image quality
Color and Texture Segmentation
Objective: Segment objects from an image based on color and texture. Use statistical measures to characterize texture features and measure texture similarity between images.Transforming between image color spacesSegmenting objects based on color attributes and color differenceSegmenting objects based on texture using nonlinear filtersAnalyzing image texture using statistical measures like contrast and correlation
Improving Segmentation
Objective: Improve binary segmentation results by refining the segmentation mask. Use interactive and iterative techniques to segment image regions.Using morphological operations to refine segmentation masksSegmenting images and refining results interactivelyUsing iterative techniques to evolve segmentation from a seed
Finding and Analyzing Objects
Objective: Count and label objects detected in a segmentation. Measure object properties like area, perimeter, and centroids.Extracting and labeling objects in a segmentation maskMeasuring shape propertiesSeparating adjacent and overlapping objects with watershed transform
Detecting Edges and Shapes
Objective: Detect edges of objects and extract boundary pixel locations. Detect objects by shapes such as lines and circles.Detecting object edgesIdentifying objects by detecting lines and circlesPerforming batch analysis over sets of images
Spatial Transformation and Image Registration
Objective: Compare images with different scales and orientations by geometrically aligning them.Applying geometric transformations to imagesAligning images using phase correlationAligning images using point mapping
Automating Image Registration with Image Features
Objective: Detect, extract, and match sets of image features to automate image registration.Detecting and extracting featuresMatching features to estimate geometric transformation between two images