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Chapter 1: Introduction to AI and Machine Learning in Engineering
Places AI and machine learning within engineering practice, contrasting data-driven and model-driven approaches while establishing the book's learning design.
Chapter 2: Linear Algebra Essentials
Develops the vector, matrix, geometric, and decomposition tools used to represent engineering data and understand machine learning algorithms.
Chapter 3: Probability and Statistics Fundamentals
Builds the probabilistic language needed to describe uncertainty, summarize samples, perform inference, and evaluate relationships in engineering data.
Chapter 4: Optimization Basics
Introduces objectives, constraints, convexity, and numerical algorithms as the mathematical engine behind model training and engineering design decisions.
Chapter 5: Introduction to Machine Learning
Establishes the core machine learning vocabulary, learning paradigms, generalization concepts, evaluation practices, and practical Python environment.
Chapter 6: Supervised Learning: Regression
Develops regression from linear models through nonlinear basis functions and regularization, with attention to diagnostics and engineering interpretation.
Chapter 7: Supervised Learning: Classification
Frames categorical prediction and compares interpretable, geometric, and instance-based classifiers for engineering decisions.
Chapter 8: Ensemble Methods
Explains how bagging, random forests, and boosting combine models to improve predictive performance, robustness, and interpretability.
Chapter 9: Neural Networks and Deep Learning
Moves from artificial neurons and multilayer perceptrons to modern deep architectures, training practices, evaluation, and engineering deployment.
Chapter 10: Unsupervised Learning
Introduces methods for discovering structure without labels, including clustering, dimensionality reduction, density estimation, and anomaly detection.
Chapter 11: Reinforcement Learning
Develops sequential decision-making from Markov decision processes through value, policy, actor-critic, and deep reinforcement learning methods.
Chapter 12: Generative Models
Surveys probabilistic and neural approaches that generate data, designs, and engineering assistance, including VAEs, GANs, diffusion models, and LLMs.
Chapter 13: Physics-Informed Machine Learning
Combines governing equations, constraints, data, and learning algorithms to build models that respect engineering knowledge and limited-data conditions.
Chapter 14: Specialized ML Techniques and Emergent Topics
Connects a broad set of specialized methods to engineering practice, from transfer and Bayesian learning to graphs, symbolic methods, agentic AI, and MLOps.
Chapter 15: Integrating Machine Learning into Engineering Systems
Brings the methods together at system level, emphasizing paradigm selection, data-versus-physics tradeoffs, validation, deployment, trust, and human oversight.
Conceptual bridges between engineering systems and learning systems
An engineering example catalogued in Chapter 1, Introduction to AI and Machine Learning in Engineering.
Experimental laboratories used throughout the book
An engineering example catalogued in Chapter 1, Introduction to AI and Machine Learning in Engineering.
Diamond examples connect matrix structure to engineering and ML throughout the chapter
An engineering example catalogued in Chapter 2, Linear Algebra Essentials.
Diamond examples connect probability and statistics to engineering measurements and ML decisions throughout the chapter
An engineering example catalogued in Chapter 3, Probability and Statistics Fundamentals.
Diamond examples pair engineering optimization problems with machine learning objectives throughout the chapter
An engineering example catalogued in Chapter 4, Optimization Basics.
Engineering examples illustrate learning types, evaluation choices, and generalization throughout the chapter
An engineering example catalogued in Chapter 5, Introduction to Machine Learning.
Predicting tensile strength of steel
An engineering example catalogued in Chapter 6, Supervised Learning: Regression.
Estimating building heat-loss parameters
An engineering example catalogued in Chapter 6, Supervised Learning: Regression.
Battery lifetime model evaluation
An engineering example catalogued in Chapter 6, Supervised Learning: Regression.
Cantilever beam deflection
An engineering example catalogued in Chapter 6, Supervised Learning: Regression.
Automobile MPG with regularized regression
An engineering example catalogued in Chapter 6, Supervised Learning: Regression.
Mechanical failure status with logistic regression
An engineering example catalogued in Chapter 7, Supervised Learning: Classification.
Concrete strength classes with decision trees
An engineering example catalogued in Chapter 7, Supervised Learning: Classification.
Rotating equipment fault classification with SVM
An engineering example catalogued in Chapter 7, Supervised Learning: Classification.
Air quality monitoring with K-NN
An engineering example catalogued in Chapter 7, Supervised Learning: Classification.
Bagging for steel plate quality control
An engineering example catalogued in Chapter 8, Ensemble Methods.
Random forest for equipment failure prediction
An engineering example catalogued in Chapter 8, Ensemble Methods.
Gradient boosting for fault detection
An engineering example catalogued in Chapter 8, Ensemble Methods.
Evaluating and interpreting a random forest
An engineering example catalogued in Chapter 8, Ensemble Methods.
Manufacturing quality classification with MLP
An engineering example catalogued in Chapter 9, Neural Networks and Deep Learning.
Concrete strength prediction
An engineering example catalogued in Chapter 9, Neural Networks and Deep Learning.
CNN surface-defect detection
An engineering example catalogued in Chapter 9, Neural Networks and Deep Learning.
LSTM predictive maintenance from sensor time series
An engineering example catalogued in Chapter 9, Neural Networks and Deep Learning.
Hydraulic operating-state clustering
An engineering example catalogued in Chapter 10, Unsupervised Learning.
Power-grid sensor fault detection with DBSCAN
An engineering example catalogued in Chapter 10, Unsupervised Learning.
Vibration fault diagnosis with PCA
An engineering example catalogued in Chapter 10, Unsupervised Learning.
Structural health monitoring with autoencoders
An engineering example catalogued in Chapter 10, Unsupervised Learning.
Q-learning for adaptive cruise control
An engineering example catalogued in Chapter 11, Reinforcement Learning.
SARSA traffic-light control
An engineering example catalogued in Chapter 11, Reinforcement Learning.
Actor-critic chemical reactor control
An engineering example catalogued in Chapter 11, Reinforcement Learning.
DDPG robot-arm control
An engineering example catalogued in Chapter 11, Reinforcement Learning.
VAE sensor anomaly detection
An engineering example catalogued in Chapter 12, Generative Models.
GAN surface-defect augmentation
An engineering example catalogued in Chapter 12, Generative Models.
Diffusion-based microstructure synthesis
An engineering example catalogued in Chapter 12, Generative Models.
LLM copilot for cantilever beam sizing
An engineering example catalogued in Chapter 12, Generative Models.
Physics-constrained capacitor discharge regression
An engineering example catalogued in Chapter 13, Physics-Informed Machine Learning.
Mass-spring fault classification
An engineering example catalogued in Chapter 13, Physics-Informed Machine Learning.
PINN solution of a one-dimensional PDE
An engineering example catalogued in Chapter 13, Physics-Informed Machine Learning.
Transfer learning for manufacturing defect detection
An engineering example catalogued in Chapter 14, Specialized ML Techniques and Emergent Topics.
Graph convolution for mesh stress prediction
An engineering example catalogued in Chapter 14, Specialized ML Techniques and Emergent Topics.
Self-supervised bearing degradation representations
An engineering example catalogued in Chapter 14, Specialized ML Techniques and Emergent Topics.
Symbolic discovery of nonlinear mechanical equations
An engineering example catalogued in Chapter 14, Specialized ML Techniques and Emergent Topics.
Agentic support for early-stage engineering design
An engineering example catalogued in Chapter 14, Specialized ML Techniques and Emergent Topics.
System-level design cases connect learning components to validation, deployment, digital twins, and governance
An engineering example catalogued in Chapter 15, Integrating Machine Learning into Engineering Systems.