MATLAB for Machine Learning: Unlock the power of deep learning for swift and enhanced results [2 ed.] 9781835087695

Table of contents :
MATLAB for Machine Learning
Contributors
About the author
About the reviewers
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
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Part 1: Getting Started with Matlab
1
Exploring MATLAB for Machine Learning
Technical requirements
Introducing ML
How to define ML
Analysis of logical reasoning
Learning strategy typologies
Discovering the different types of learning processes
Supervised learning
Unsupervised learning
Reinforcement learning
Semi-supervised learning
Transfer learning
Using ML techniques
Selecting the ML paradigm
Step-by-step guide on how to build ML models
Exploring MATLAB toolboxes for ML
Statistics and Machine Learning Toolbox
Deep Learning Toolbox
Reinforcement Learning Toolbox
Computer Vision Toolbox
Text Analytics Toolbox
ML applications in real life
Summary
2
Working with Data in MATLAB
Technical requirements
Importing data into MATLAB
Exploring the Import Tool
Using the load() function to import files
Reading ASCII-delimited files
Exporting data from MATLAB
Working with different types of data
Working with images
Audio data handling
Exploring data wrangling
Introducing data cleaning
Discovering exploratory statistics
EDA
EDA in practice
Introducing exploratory visualization
Understanding advanced data preprocessing techniques in MATLAB
Data normalization for feature scaling
Introducing correlation analysis in MATLAB
Summary
Part 2: Understanding Machine Learning Algorithms in MATLAB
3
Prediction Using Classification and Regression
Technical requirements
Introducing classification methods using MATLAB
Decision trees for decision-making
Exploring decision trees in MATLAB
Building an effective and accurate classifier
SVMs explained
Supervised classification using SVM
Exploring different types of regression
Introducing linear regression
Linear regression model in MATLAB
Making predictions with regression analysis in MATLAB
Multiple linear regression with categorical predictor
Evaluating model performance
Reducing outlier effects
Using advanced techniques for model evaluation and selection in MATLAB
Understanding k-fold cross-validation
Exploring leave-one-out cross-validation
Introducing the bootstrap method
Summary
4
Clustering Analysis and Dimensionality Reduction
Technical requirements
Understanding clustering – basic concepts and methods
How to measure similarity
How to find centroids and centers
How to define a grouping
Understanding hierarchical clustering
Partitioning-based clustering algorithms with MATLAB
Introducing the k-means algorithm
Using k-means in MATLAB
Grouping data using the similarity measures
Applying k-medoids in MATLAB
Discovering dimensionality reduction techniques
Introducing feature selection methods
Exploring feature extraction algorithms
Feature selection and feature extraction using MATLAB
Stepwise regression for feature selection
Carrying out PCA
Summary
5
Introducing Artificial Neural Network Modeling
Technical requirements
Getting started with ANNs
Basic concepts relating to ANNs
Understanding how perceptrons work
Activation function to introduce non-linearity
ANN’s architecture explained
Training and testing an ANN model in MATLAB
How to train an ANN
Introducing the MATLAB Neural Network Toolbox
Understanding data fitting with ANNs
Discovering pattern recognition using ANNs
Building a clustering application with an ANN
Exploring advanced optimization techniques
Understanding SGD
Exploring Adam optimization
Introducing second-order methods
Summary
6
Deep Learning and Convolutional Neural Networks
Technical requirements
Understanding DL basic concepts
Automated feature extraction
Training a DNN
Exploring DL models
Approaching CNNs
Convolutional layer
Pooling layer
ReLUs
FC layer
Building a CNN in MATLAB
Exploring the model’s results
Discovering DL architectures
Understanding RNNs
Analyzing LSTM networks
Introducing transformer models
Summary
Part 3: Machine Learning in Practice
7
Natural Language Processing Using MATLAB
Technical requirements
Explaining NLP
NLA
NLG
Analyzing NLP tasks
Introducing automatic processing
Exploring corpora and word and sentence tokenizers
Corpora
Words
Sentence tokenize
Implementing a MATLAB model to label sentences
Introducing sentiment analysis
Movie review sentiment analysis
Using an LSTM model for label sentences
Understanding gradient boosting techniques
Approaching ensemble learning
Bagging definition and meaning
Discovering random forest
Boosting algorithms explained
Summary
8
MATLAB for Image Processing and Computer Vision
Technical requirements
Introducing image processing and computer vision
Understanding image processing
Explaining computer vision
Exploring MATLAB tools for computer vision
Building a MATLAB model for object recognition
Introducing handwriting recognition (HWR)
Training and fine-tuning pretrained deep learning models in MATLAB
Introducing the ResNet pretrained network
The MATLAB Deep Network Designer app
Interpreting and explaining machine learning models
Understanding saliency maps
Understanding feature importance scores
Discovering gradient-based attribution methods
Summary
9
Time Series Analysis and Forecasting with MATLAB
Technical requirements
Exploring the basic concepts of time series data
Understanding predictive forecasting
Introducing forecasting methodologies
Time series analysis
Extracting statistics from sequential data
Converting a dataset into a time series format in MATLAB
Understanding time series slicing
Resampling time series data in MATLAB
Moving average
Exponential smoothing
Implementing a model to predict the stock market
Dealing with imbalanced datasets in MATLAB
Understanding oversampling
Exploring undersampling
Summary
10
MATLAB Tools for Recommender Systems
Technical requirements
Introducing the basic concepts of recommender systems
Understanding CF
Content-based filtering explained
Hybrid recommender systems
Finding similar users in data
Creating recommender systems for network intrusion detection using MATLAB
Recommender system for NIDS
NIDS using a recommender system in MATLAB
Deploying machine learning models
Understanding model compression
Discovering model pruning techniques
Introducing quantization for efficient inference on edge devices
Getting started with knowledge distillation
Learning low-rank approximation
Summary
11
Anomaly Detection in MATLAB
Technical requirements
Introducing anomaly detection and fault diagnosis systems
Anomaly detection overview
Fault diagnosis systems explained
Approaching fault diagnosis using ML
Using ML to identify anomalous functioning
Anomaly detection using logistic regression
Improving accuracy using the Random Forest algorithm
Building a fault diagnosis system using MATLAB
Understanding advanced regularization techniques
Understanding dropout
Exploring L1 and L2 regularization
Introducing early stopping
Summary
Index
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