Machine Learning for Transportation Research and Applications - Cui, Zhiyong
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Présentation Machine Learning For Transportation Research And Applications Format Broché
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Résumé : Part One: Overview Part Two: Methodologies and Applications Part Three: Future Research and Applications
1. General Introduction and Overview
2. Fundamental Mathematics
3. Machine Learning Basics
4. Classical ML Methods
5. Convolutional Neural Network
6. Graph Neural Network
7. Sequence Modeling
8. Probabilistic Models
9. Reinforcement Learning
10. Generative Models
11. Meta/Transfer Learning
The Future of Transportation and AI
Biographie:
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Sommaire:
Yinhai Wang - Ph.D., P.E., Professor, Transportation Engineering, University of Washington, USA. Dr. Yinhai Wang is a fellow of both the IEEE and American Society of Civil Engineers (ASCE). He also serves as director for Pacific Northwest Transportation Consortium (PacTrans), USDOT University Transportation Center for Federal Region 10, and the Northwestern Tribal Technical Assistance Program (NW TTAP) Center. He earned his Ph.D. in transportation engineering from the University of Tokyo (1998) and a Master in Computer
Science from the UW (2002). Dr. Wang's research interests include traffic sensing, transportation data science, artificial intelligence methods and applications, edge computing, traffic operations and simulation, smart urban mobility, transportation safety, among others....