Machine Learning for Sustainable Transport Solutions
( 24 Modules )
Module #1 Introduction to Sustainable Transportation Overview of sustainable transportation, its importance, and the role of machine learning in achieving it
Module #2 Machine Learning Fundamentals Introduction to machine learning concepts, types of machine learning, and key algorithms
Module #3 Data Preprocessing for Transportation Data Importance of data preprocessing, data cleaning, feature scaling, and feature selection for transportation data
Module #4 Transportation Data Sources and Formats Overview of transportation data sources, formats, and common data standards
Module #5 Introduction to Python for Machine Learning Introduction to Python, popular libraries (NumPy, Pandas, Matplotlib), and setup for development environment
Module #6 Supervised Learning for Transportation Applying supervised learning techniques to transportation problems, including regression and classification
Module #7 Unsupervised Learning for Transportation Applying unsupervised learning techniques to transportation problems, including clustering and dimensionality reduction
Module #8 Reinforcement Learning for Transportation Applying reinforcement learning techniques to transportation problems, including simulation-based optimization
Module #9 Deep Learning for Transportation Applying deep learning techniques to transportation problems, including computer vision and natural language processing
Module #10 Traffic Flow Prediction Using machine learning to predict traffic flow, including data preparation and model evaluation
Module #11 Route Optimization Using machine learning to optimize routes, including dynamic route optimization and route planning
Module #12 Transportation Network Analysis Using machine learning to analyze transportation networks, including graph-based algorithms
Module #13 Electric Vehicle Charging Infrastructure Optimization Using machine learning to optimize electric vehicle charging infrastructure, including demand forecasting and charging station placement
Module #14 Shared Mobility Services Optimization Using machine learning to optimize shared mobility services, including ride-hailing and bike-sharing
Module #15 Autonomous Vehicles and Machine Learning Overview of autonomous vehicles and the role of machine learning in their development
Module #16 Transportation Safety Analysis Using machine learning to analyze transportation safety, including accident prediction and risk assessment
Module #17 Transportation Emissions Modeling Using machine learning to model transportation emissions, including emissions forecasting and mitigation strategies
Module #18 Sustainable Transportation Policy Analysis Using machine learning to analyze the impact of sustainable transportation policies, including policy evaluation and optimization
Module #19 Case Study:Intelligent Transportation Systems Real-world case study of applying machine learning to intelligent transportation systems
Module #20 Case Study:Electric Vehicle Adoption Real-world case study of applying machine learning to electric vehicle adoption and charging infrastructure planning
Module #21 Case Study:Shared Mobility Services Real-world case study of applying machine learning to shared mobility services, including ride-hailing and bike-sharing
Module #22 Ethics and Fairness in Transportation Machine Learning Importance of ethics and fairness in transportation machine learning, including bias detection and mitigation
Module #23 Explainability and Interpretability in Transportation Machine Learning Importance of explainability and interpretability in transportation machine learning, including model interpretability techniques
Module #24 Course Wrap-Up & Conclusion Planning next steps in Machine Learning for Sustainable Transport Solutions career
