book

Machine Learning in Transportation

Name: Machine Learning in Transportation
ISBN: 9783110788778

by Niharika Dayyala, Nivedya Madankara Kottayi, Rajib Basu Mallick

December 2024

Intermediate to advanced

170 pages

4h 11m

English

De Gruyter

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Chapter 1 Introduction to machine learning
1.1 What is machine learning1.2 Types of machine learning algorithms1.3 Core concepts in machine learning1.3.1 Classification1.3.2 Numerical prediction1.3.3 Dimension reduction1.4 Fundamentals of probability, statistics, and linear algebra1.4.1 Statistical concepts1.5 Linear algebra: matrices
Chapter 2 Preliminary steps for machine learning
2.1 Introduction2.1.1 Getting the software tools: MATLAB and R2.2 Data exploration2.2.1 Data Exploration with R2.2.2 Filtering and counting with R2.2.3 Sorting with R2.3 Data cleaning2.3.1 Working with missing values in R2.3.2 Dealing with outliers in R2.3.3 Creating subsets in R2.4 Data transformations2.4.1 Assigning numerical values to categorical data using R2.4.2 Assigning bins to numeric data with R2.4.3 Data standardization with R2.5 Data visualization with R2.6 Basic statistical analysis with MATLAB
Chapter 3 Predictive performance evaluation
3.1 Accuracy for the prediction of numerical values3.1.1 Mean squared error (MSE)3.1.2 R2 (coefficient of determination)3.2 Accuracy for classification3.2.1 Confusion matrix3.2.2 Accuracy3.2.3 Precision3.2.4 Recall (sensitivity or true positive rate)3.2.5 F1 score
Chapter 4 Supervised learning models
4.1 K-nearest neighbors4.1.1 Classification with KNN in R4.1.2 Numerical prediction with KNN in R4.2 Analysis with MATLAB4.2.1 KNN model4.2.2 Naïve Bayes’ model4.2.3 Support vector machine (SVM)4.2.4 Decision tree4.2.5 Linear regression4.2.6 Neural network4.2.7 Gradient descent algorithm
Chapter 5 Unsupervised learning model
5.1 Dimension reduction using principal component analysis (PCA)5.2 Cluster analysis5.2.1 K-means cluster analysis5.2.2 K-Means clustering on numerical data with R5.2.3 K-medoids method (Partitioning Around Medoids) on categorical data with R5.2.4 Hierarchical Clustering5.2.5 Hierarchical clustering on numerical data with R5.2.6 Hierarchical clustering on categorical data with R5.3 Clustering with MATLAB
Chapter 6 Deep learning
6.1 Transfer learning6.2 Long short-term memory (LSTM)
Chapter 7 Text mining
7.1 Introduction7.2 Text extraction7.2.1 Extracting data from a pdf file7.2.2 Creating bag of words, term document matrix, and word frequencies7.2.3 Creating word cloud7.2.4 Sentiment analysis7.2.5 Topic modeling and clusteringReferences
Index

Overview

The book introduces the reader to machine learning in transportation. It discusses both simple and advanced concepts including core statistics, data wrangling, data visualization, supervised and unsupervised datamining techniques as well as text mining. The book prepares students to manage data, visualize data and apply appropriate machine learning techniques on transportation datasets to derive important insights.

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