MATLAB for Machine Learning - Second Edition: Unlock the power of deep learning for swift and enhanced results

Cover
Title Page
Copyright and Credits
Contributors
Table of Contents
Preface
Part 1: Getting Started with Matlab
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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
Chapter 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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