AIPrimer.AI
  • 🚦AI Primer In Transportation
  • CHAPTER 1 - INTRODUCTION TO MACHINE LEARNING
    • Machine Learning in Transportation
    • What is Machine Learning?
    • Types of Machine Learning
      • Supervised Learning
      • Unsupervised Learning
      • Semi-supervised Learning
      • Reinforced Learning
    • Fundamental concepts of machine learning
      • Model Training and Testing
      • Evaluating the Model’s Prediction Accuracy
      • The Underfitting and Overfitting Problems
      • Bias-Variance Tradeoff in Overfitting
      • Model Validation Techniques
      • Hyperparameter Tuning
      • Model Regularization
      • The Curse of Ddimensionality
    • Machine Learning versus Statistics
  • CHAPTER 2 - SUPERVISED METHODS
    • Supervised Learning_Complete Draft
    • K-Nearest Neighbor (KNN) Algorithm
    • Tree-Based Methods
    • Boosting
    • Support Vector Machines (SVMs)
  • CHAPTER 3 - UNSUPERVISED LEARNING
    • Principal Component Analysis
      • How Does It Work?
      • Interpretation of PCA result
      • Applications in Transportation
    • CLUSTERING
      • K-MEANS
      • SPECTRAL CLUSTERING
      • Hierarchical Clustering
    • REFERENCE
  • CHAPTER 4 - NEURAL NETWORK
    • The Basic Paradigm: Multilayer Perceptron
    • Regression and Classification Problems with Neural Networks
    • Advanced Topologies
      • Modular Network
      • Coactive Neuro–Fuzzy Inference System
      • Recurrent Neural Networks
      • Jordan-Elman Network
      • Time-Lagged Feed-Forward Network
      • Deep Neural Networks
  • CHAPTER 5 - DEEP LEARNING
    • Convolutional Neural Networks
      • Introduction
      • Convolution Operation
      • Typical Layer Structure
      • Parameters and Hyperparameters
      • Summary of Key Features
      • Training of CNN
      • Transfer Learning
    • Recurrent Neural Networks
      • Introduction
      • Long Short-Term Memory Neural Network
      • Application in transportation
    • Recent Development
      • AlexNet, ZFNet, VggNet, and GoogLeNet
      • ResNet
      • U-Net: Full Convolutional Network
      • R-CNN, Fast R-CNN, and Faster R-CNN
      • Mask R-CNN
      • SSD and YOLO
      • RetinaNet
      • MobileNets
      • Deformable Convolution Networks
      • CenterNet
      • Exemplar Applications in Transportation
    • Reference
  • CHAPTER 6 - REINFORCEMENT LEARNING
    • Introduction
    • Reinforcement Learning Algorithms
    • Model-free v.s. Model-based Reinforcement Learning
    • Applications of Reinforcement Learning to Transportation and Traffic Engineering
    • REFERENCE
  • CHAPTER 7 - IMPLEMENTING ML AND COMPUTATIONAL REQUIREMENTS
    • Data Pipeline for Machine Learning
      • Introduction
      • Problem Definition
      • Data Ingestion
      • Data Preparation
      • Data Segregation
      • Model Training
      • Model Deployment
      • Performance Monitoring
    • Implementation Tools: The Machine Learning Ecosystem
      • Machine Learning Framework
      • Data Ingestion tools
      • Databases
      • Programming Languages
      • Visualization Tools
    • Cloud Computing
      • Types and Services
    • High-Performance Computing
      • Deployment on-premise vs on-cloud
      • Case Study: Data-driven approach for the implementation of Variable Speed Limit
      • Conclusion
  • CHAPTER 8 - RESOURCES
    • Mathematics and Statistics
    • Programming, languages, and software
    • Machine learning environments
    • Tools of the Trade
    • Online Learning Sites
    • Key Math Concepts
  • REFERENCES
  • IMPROVEMENT BACKLOG
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REFERENCES

AA. Kohavi and F. Provost, "Glossary of terms," Machine Learning, vol. 30, no. 2–3, pp. 271–274, 1998.

BB. Powers, David M W (2011). "Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation". Journal of Machine Learning Technologies. 2 (1): 37–63.

CC. Fawcett, Tom (2006). "An Introduction to ROC Analysis". Pattern Recognition Letters. 27 (8): 861–874. doi:10.1016/j.patrec.2005.10.010.

DD. Christian, Brian; Griffiths, Tom (April 2017), "Chapter 7: Overfitting", Algorithms To Live By: The computer science of human decisions, William Collins, pp. 149–168, ISBN 978-0-00-754799-9.

EE. Rokach, L. (2010). "Ensemble-based classifiers". Artificial Intelligence Review. 33 (1–2): 1–39. doi:10.1007/s10462-009-9124-7.

FF. Prechelt, Lutz; Geneviève B. Orr (2012-01-01). "Early Stopping — But When?". In Grégoire Montavon; Klaus-Robert Müller (eds.). Neural Networks: Tricks of the Trade. Lecture Notes in Computer Science. Springer Berlin Heidelberg. pp. 53–67. doi:10.1007/978-3-642-35289-8_5. ISBN 978-3-642-35289-8.

JJ. BĂĽhlmann, Peter; Van De Geer, Sara (2011). "Statistics for High-Dimensional Data". Springer Series in Statistics: 9. doi:10.1007/978-3-642-20192-9.

Hastie, T., Tibshirani, R., Friedman, J., 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition, 2nd ed, Springer Series in Statistics. Springer-Verlag, New York.

Scikit-learn developers, 2019. Cross-validation: evaluating estimator performance — scikit-learn 0.21.3 documentation [WWW Document]. URL https://scikit-learn.org/stable/modules/cross_validation.html (accessed 10.5.19).

James, G., Witten, D., Hastie, T. and Tibshirani, R., 2013. An introduction to statistical learning (Vol. 112, p. 18). New York: springer.

Goodfellow, I., Bengio, Y. and Courville, A., 2016. Deep learning. MIT press.

Xu, D. and Tian, Y., 2015. A comprehensive survey of clustering algorithms. Annals of Data Science, 2(2), pp.165-193.

Sorzano, C.O.S., Vargas, J. and Montano, A.P., 2014. A survey of dimensionality reduction techniques. arXiv preprint arXiv:1403.2877.

Khan, S.M., Dey, K.C. and Chowdhury, M., 2017. Real-time traffic state estimation with connected vehicles. IEEE Transactions on Intelligent Transportation Systems, 18(7), pp.1687-1699.

Khan, S.M., Islam, S., Khan, M.Z., Dey, K., Chowdhury, M., Huynh, N. and Torkjazi, M., 2018. Development of Statewide Annual Average Daily Traffic Estimation Model from Short-Term Counts: A Comparative Study for South Carolina. Transportation Research Record, 2672(43), pp.55-64.

Khan, S.M., Chowdhury, M., Ngo, L.B. and Apon, A., 2020. Multi-class twitter data categorization and geocoding with a novel computing framework. Cities, 96, p.102410.

Tan, B., Zhang, J. and Wang, L., 2011. Semi-supervised elastic net for pedestrian counting. Pattern Recognition, 44(10-11), pp.2297-2304.

Liu, T., Yang, Y., Huang, G.B. and Lin, Z., 2015. Detection of drivers’ distraction using semi-supervised extreme learning machine. In Proceedings of ELM-2014 Volume 2 (pp. 379-387). Springer, Cham.

Chakraborty, P., Sharma, A. and Hegde, C., 2018, November. Freeway traffic incident detection from cameras: A semi-supervised learning approach. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC) (pp. 1840-1845). IEEE.

Peters, Vijayakumar, and Schaal, “Natural Actor-Critic.”

Williams, “Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning.

Mnih et al., “Playing Atari with Deep Reinforcement Learning.”

Van der Pol and Oliehoek, “Coordinated Deep Reinforcement Learners for Traffic Light Control. Zhu and Ukkusuri, “A Reinforcement Learning Approach for Distance-Based Dynamic Tolling in the Stochastic Network Environment.

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https://en.wikipedia.org/wiki/Supervised_learning
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