Introduction to Machine Learning

The activity aligns closely with the goals and objectives of the Smart Droplets project. By covering the main pillars of Machine Learning, specifically in the context of computer vision methods, the event directly relates to the project’s focus on developing AI anomaly detection systems and optimizing AI models for edge devices. Understanding different types of machine learning, such as supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning, is crucial for the development of AI models within the Smart Droplets project. These models will be trained and optimized to detect anomalies and provide dosage recommendations in real-time using computer vision techniques. The optimization methods mentioned, including fine-tuning model parameters to minimize loss functions such as Mean Squared Error (MSE) and Cross-Entropy loss, are essential for optimizing the performance of AI models deployed in the Smart Droplets system. By fine-tuning the models using these methods, the project aims to achieve high accuracy and real-time performance while operating on edge devices. The activity also mentions various other components such as datasets, evaluation metrics, and data pre-processing. These components are crucial in the development and implementation of AI models within the Smart Droplets project. The project relies on curated datasets, appropriate evaluation metrics, and effective data pre- processing techniques to train and assess the performance of the AI models used for anomaly detection and dosage recommendation.