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Full Description
Understanding Models Developed by AI: Including Applications with Python and MATLAB Code is a comprehensive guide on the intricacies of AI models and their real-world applications. The book demystifies complex AI methodologies by providing clear explanations and practical examples that are reinforced with Python and MATLAB program codes. Its content structure emphasizes a practical, applications-driven approach to understanding AI models, with hands-on coding examples throughout each chapter. Readers will find the tools they need to build AI models, along with the knowledge to make these models accessible and interpretable to stakeholders, thus fostering trust and reliability in AI systems.
As the primary issues with the adoption of AI/ML models are reliability, transparency, interpretation of results, and bias (data and algorithm) management, this resource give researchers and developers what they need to be able to not only implement AI models, but also interpret and explain them. This is crucial in industries where decision-making processes must be transparent and understandable.
Contents
1. Introduction: Understanding AI Models: Overview of AI and the critical importance of model interpretability.
2. Techniques for Model Explanation: Various methods to enhance AI model transparency and interpretability.
3. Feature Selection and Data Augmentation: Techniques for choosing relevant features and enhancing data quality to improve AI models.
4. Understanding Performance Metrics: Key error metrics in AI and how to interpret them to evaluate model performance.
5. Interpreting Classification Models: Understanding and applying classification models with practical examples.
6. Interpreting Regression Models: Techniques for making sense of continuous predictions in regression models.
7. Interpreting Clustering Models: Discovering patterns with clustering techniques and interpreting results.
8. Interpreting Reinforcement Learning Models: Understanding decision-making processes in reinforcement learning.
9. Interpreting Artificial Neural Networks: Techniques for demystifying neural networks and explaining their workings.
10. Interpreting Deep Learning Models: Exploring advanced deep learning techniques with a focus on interpretability.
11. AI Ethics and Responsible Use: Ethical considerations in AI, focusing on the implications of model interpretability.
