Artificial Intelligence I ISO/IEC 22989:2022 Training – Part 7 | Clause 5.12.1.3 to 5.13 Explained скачать в хорошем качестве

Artificial Intelligence I ISO/IEC 22989:2022 Training – Part 7 | Clause 5.12.1.3 to 5.13 Explained

Edicent Quality Registrar (EQR) Services: Certification, Training and Advising Contact Details: +91-8802650960; info@edicentcertification.org In this part of the Thorough Training on ISO/IEC 22989:2022, we explore advanced AI concepts and machine learning algorithms that form the technical foundation of modern artificial intelligence systems. This session focuses on key algorithmic approaches and the conceptual relationship between autonomy, heteronomy, and automation in intelligent systems. 🔹 Examples of Machine Learning Algorithms Neural Networks Neural networks are computational models inspired by biological neural systems and are widely used to enable intelligent capabilities in AI systems. Recurrent Neural Networks (RNNs) are designed to process ordered sequences of data, where the order of inputs matters. RNNs maintain a stateful property, allowing information from previous passes or layers to influence future predictions. This makes them particularly useful for dynamic and sequential data such as speech, time-series analysis, and language processing. Long Short-Term Memory (LSTM) networks are a specialized form of RNN designed to capture long-term dependencies in data. They help overcome the vanishing gradient problem and allow AI systems to learn complex sequences and long-range relationships. Convolutional Neural Networks (CNNs) are widely used in image processing and computer vision. These networks contain at least one convolutional layer that applies filters to extract useful features and patterns from input data. Bayesian Networks Bayesian networks are probabilistic graphical models that represent relationships between variables. They use probability theory to perform prediction, inference, and decision-making under uncertainty. Decision Trees Decision trees are structured models used for classification and regression tasks. They follow a tree-like structure consisting of nodes and branches: Decision nodes represent conditions that split the data. Branches represent possible outcomes of a decision. Leaf nodes represent the final classification or prediction. These models resemble flowcharts, making them intuitive and easy to interpret. Support Vector Machines (SVM) Support Vector Machines are powerful machine learning methods used for classification and regression analysis. They work by identifying a hyperplane that separates data points belonging to different categories. Key characteristics include: Maximum-margin classification, where the algorithm finds the optimal boundary that maximizes the distance between classes. Use of kernel functions to handle non-linear patterns. Concepts such as hard margin and soft margin classification. SVMs are widely used for pattern recognition, categorization of unlabeled data, and predictive modeling. 🔹 Autonomy, Heteronomy, and Automation in AI Systems Another key topic in this session is the relationship between autonomy, heteronomy, and automation in intelligent systems. These concepts help classify how AI systems operate depending on their level of independence and external control. Key classification criteria include: External supervision – the degree to which a system requires human oversight or control. Situated understanding – the system’s ability to interpret and respond to its environment. Reactivity – how quickly and effectively the system responds to environmental changes. Based on these criteria, systems can range from no automation and operator assistance to partial automation, conditional automation, high automation, full automation, and ultimately autonomous systems capable of adapting and operating without external intervention. 🎯 What You Will Learn in This Part Core machine learning algorithms used in AI systems The working principles of RNN, LSTM, and CNN architectures Fundamentals of Bayesian networks, decision trees, and support vector machines The conceptual framework of autonomy, heteronomy, and automation How levels of automation define intelligent system behavior This training helps professionals, researchers, and practitioners understand the standardized AI terminology and conceptual frameworks defined in ISO/IEC 22989:2022, enabling consistent communication and effective AI system development. Compliance of any international standard has three pillars management team, audit, and training only, it adds more valuable than marketing in short and long term run, compliance importance and usefulness is all belong to a business internally itself not on external dependence. You may connect for our service at www.edicentcertification.org, please like, subscribe and share. Bank account details for your support EQR Account Detail: Bank Name: HDFC Bank Current Account Name: Edicent Quality Registrar Current Account Number: 50200086783433 IFSC Code: HDFC0005269 SWIFT Code: HDFCINBBDEL UPI ID: 8882814173@hdfcbank Paypal ID: https://paypal.me/EQRQuality Thanks