Intelligent Waste Classification with Computer Vision
( 30 Modules )
Module #1 Introduction to Intelligent Waste Classification Overview of the importance of waste classification, benefits of intelligent waste classification, and introduction to computer vision
Module #2 Waste Classification Challenges and Opportunities Discussion of current waste classification methods, challenges, and opportunities for improvement with AI
Module #3 Computer Vision Fundamentals Introduction to computer vision concepts, including image processing, object detection, and image classification
Module #4 Image Acquisition and Preprocessing for Waste Classification Discussion of image acquisition techniques, image quality enhancement, and preprocessing methods for waste classification
Module #5 Convolutional Neural Networks (CNNs) for Image Classification Introduction to CNNs, architecture, and applications for image classification
Module #6 Transfer Learning for Waste Classification Discussion of transfer learning, fine-tuning pre-trained models, and its application to waste classification
Module #7 Waste Classification Datasets and Data Augmentation Overview of publicly available waste classification datasets, data augmentation techniques, and data preparation
Module #8 Implementing Waste Classification with TensorFlow/Keras Hands-on implementation of waste classification using TensorFlow/Keras
Module #9 Evaluation Metrics for Waste Classification Discussion of evaluation metrics for waste classification models, including accuracy, precision, recall, and F1-score
Module #10 Hyperparameter Tuning for Waste Classification Models Introduction to hyperparameter tuning, grid search, and random search for optimizing waste classification models
Module #11 Object Detection for Waste Classification Introduction to object detection, YOLO, SSD, and Faster R-CNN for waste classification
Module #12 Implementing Object Detection for Waste Classification Hands-on implementation of object detection for waste classification using TensorFlow/Keras
Module #13 Waste Classification with Deep Learning Architectures Discussion of advanced deep learning architectures for waste classification, including attention mechanisms and graph neural networks
Module #14 Case Studies:Successful Implementations of Intelligent Waste Classification Real-world case studies of successful implementations of intelligent waste classification systems
Module #15 Challenges and Future Directions in Intelligent Waste Classification Discussion of current challenges, limitations, and future directions in intelligent waste classification
Module #16 Edge Computing and IoT for Real-time Waste Classification Introduction to edge computing, IoT, and real-time waste classification systems
Module #17 Ethical Considerations in Intelligent Waste Classification Discussion of ethical considerations, fairness, and bias in intelligent waste classification systems
Module #18 Project Development and Deployment Guided project development and deployment of an intelligent waste classification system
Module #19 Waste Classification for Specific Waste Streams Discussion of waste classification for specific waste streams, including textiles, organics, and recyclables
Module #20 Waste Classification for Emerging Waste Types Discussion of waste classification for emerging waste types, including e-waste, batteries, and medical waste
Module #21 Collaborative Filtering for Waste Classification Introduction to collaborative filtering, recommender systems, and its application to waste classification
Module #22 Explainable AI for Waste Classification Discussion of explainable AI, model interpretability, and its importance in waste classification
Module #23 Human-in-the-Loop for Waste Classification Introduction to human-in-the-loop systems, active learning, and its application to waste classification
Module #24 Waste Classification for Disaster Response and Recovery Discussion of waste classification for disaster response and recovery, including rapid waste assessment and prioritization
Module #25 Waste Classification for Waste-to-Resource Systems Discussion of waste classification for waste-to-resource systems, including waste-to-energy and waste-to-chemicals
Module #26 Waste Classification for Circular Economy Applications Discussion of waste classification for circular economy applications, including product design, recycling, and upcycling
Module #27 Waste Classification Policy and Regulations Overview of waste classification policy and regulations, including EUs Waste Framework Directive and US EPA regulations
Module #28 Waste Classification Standards and Certification Discussion of waste classification standards, certification schemes, and its importance for industry adoption
Module #29 Waste Classification Business Models and Economics Discussion of waste classification business models, cost-benefit analysis, and economic feasibility
Module #30 Course Wrap-Up & Conclusion Planning next steps in Intelligent Waste Classification with Computer Vision career
