introduction
exploratory data analysis (and visualization)
data preparation
clustering
decision trees
dimension reduction
ensemble methods
feature engineering
linear regression
logistic regression
nearest neighbors
neural networks
non-linear regression
optimization
support vector machines
time series
validation


1. Introduction
What is Machine Learning?
Traditional vs. Data-Driven Approaches in Civil Engineering
Supervised, Unsupervised, and Reinforcement Learning Overview
Key Civil Engineering Applications
Pavement Condition Prediction
Structural Health Monitoring
Hydrological Forecasting
Case Study: Machine Learning for Bridge Inspection
(JupyterBook Chapter: introduction)
2. Exploratory Data Analysis and Preparation
2.1 Exploratory Data Analysis (EDA)
Understanding Data Types: Numerical vs. Categorical
Descriptive Statistics & Summary Metrics
Feature Distributions and Correlations
Visualization Techniques for Civil Engineers
Histograms, Boxplots, Scatterplots
Heatmaps for Correlation Analysis
Time Series Plots for Hydrological Data
(JupyterBook Chapter: exploratory data analysis (and visualization))
2.2 Data Preparation
Handling Missing Data
Imputation Strategies (Mean, Median, Interpolation)
Removing Outliers and Noisy Data
Data Transformation & Encoding
One-Hot Encoding for Categorical Features
Scaling & Normalization (Min-Max, Standardization)
Data Splitting for Training & Testing
(JupyterBook Chapter: data preparation)
3. Supervised Learning: Regression and Classification
3.1 Regression Models
Linear Regression: Predicting Continuous Values
Multiple Linear Regression: Civil Engineering Applications
Non-Linear Regression: When Linear Models Fail
Feature Selection & Model Complexity Tradeoff
(JupyterBook Chapters: linear regression, non-linear regression)
3.2 Classification Models
Logistic Regression: Binary & Multi-Class Classification
Decision Trees: Interpretable ML for Structural Assessment
Nearest Neighbors (KNN): Instance-Based Learning for Soil Classification
Support Vector Machines (SVM): Margin-Based Classification
Model Evaluation Metrics: Accuracy, Precision, Recall, ROC Curves
(JupyterBook Chapters: logistic regression, decision trees, nearest neighbors, support vector machines)
3.3 Model Validation and Selection
Bias-Variance Tradeoff
Cross-Validation Strategies (k-Fold, Leave-One-Out)
Hyperparameter Tuning
(JupyterBook Chapter: validation)
4. Unsupervised Learning: Clustering and Feature Reduction
4.1 Clustering Methods
K-Means Clustering: Traffic Flow Grouping
Hierarchical Clustering: Geospatial Segmentation
Density-Based Clustering (DBSCAN): Anomaly Detection
(JupyterBook Chapter: clustering)
4.2 Feature Engineering & Dimensionality Reduction
Feature Selection Methods
Removing Redundant or Irrelevant Features
Principal Component Analysis (PCA): Reducing Data Complexity
Autoencoders for Feature Learning
(JupyterBook Chapters: dimension reduction, feature engineering)
5. Advanced Learning Techniques
5.1 Neural Networks
Perceptron & Multi-Layer Perceptron (MLP)
Convolutional Neural Networks (CNNs) for Image-Based Civil Engineering
Recurrent Neural Networks (RNNs) for Time-Series Forecasting
(JupyterBook Chapter: neural networks)
5.2 Ensemble Methods
Random Forests: Combining Decision Trees
Boosting (AdaBoost, Gradient Boosting, XGBoost)
(JupyterBook Chapter: ensemble methods)
5.3 Time Series Analysis
Understanding Temporal Dependencies in Data
ARIMA & LSTMs for Hydrological Forecasting
Civil Engineering Applications: Traffic, Climate, Water Systems
(JupyterBook Chapter: time series)
5.4 Optimization Techniques
Gradient Descent & Stochastic Gradient Descent (SGD)
Sequential Feature-wise Optimization (Coordinate Descent)
Hyperparameter Search (Grid Search, Bayesian Optimization)
(JupyterBook Chapter: optimization)
6. Applications and Model Deployment
6.1 How to Select the Right Model
Tradeoffs Between Complexity, Accuracy, and Interpretability
Comparing Model Performance Across Civil Engineering Use Cases
6.2 Case Studies in Civil Engineering
Real-Time Landslide Prediction Using ML
Bridge Load Estimation with Deep Learning
Smart Traffic Signal Control via Reinforcement Learning
6.3 Model Deployment
Deploying ML Models in Civil Engineering Systems
Interfacing with APIs and Cloud-Based ML Services