Understanding Models Developed with AI
Including Applications with Python and MATLAB Code
Published on 29. May 2026
Book
Paperback/Softback
322 pages
978-0-443-44163-9 (ISBN)
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.
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.
More details
Language
English
Place of publication
San Francisco
United States
Publishing group
Elsevier Science & Technology
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 276 mm
Width: 216 mm
Weight
449 gr
ISBN-13
978-0-443-44163-9 (9780443441639)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Ömer Faruk Ertugrul | Tahir Çetin Akinci | Musa Yilmaz
Understanding Models Developed with AI
Including Applications with Python and MATLAB Code
E-Book
06/2026
Elsevier
€166.99
Available for download
Persons
OEMER FARUK ERTUGRUL received his B.S., M.S., and Ph.D. degrees in Electrical and Electronics Engineering in 2001, 2010, and 2015, respectively. His research interests are primarily focused on machine learning and signal processing. He holds six national and one international patent. He is currently a professor and vice rector at Batman University and serves as an associate editor for NC&A, covering the Middle East, excluding Iran. Moreover, he is the co-founder at INSENSE, ABRH, INTELLIGENT, and SOFTSENSE Inc. Dr. Tahir Cetin Akinci completed his undergraduate studies in the Department of Electrical Engineering at Klaipeda University, Lithuania, in 2000, followed by his master's degree in 2005 and doctorate in 2010. From 2003 to 2010, he served as a Research Assistant at Marmara University, after which he joined the Department of Electrical Engineering at Istanbul Technical University (ITU), where he earned the title of Associate Professor and, in 2020, was promoted to Professor. At ITU, Dr. Akinci has held various administrative positions, including Deputy Director of the Institute of Science and Technology and Associate Dean of the Faculty of Electrical and Electronics Engineering. Between 2021 and 2023, Dr. Akinci was a visiting scholar at the University of California, Riverside (UCR), where he currently serves as an Assistant Project Scientist. His research interests include artificial neural networks, deep learning, machine learning, cognitive systems, signal processing, data analysis, and electrical energy systems. Musa Yilmaz received his Associate Professor certificate in Electrical-Electronics and Communication Engineering. He works at the University of California, Riverside, and Batman University. He received his M.Sc. degree from Marmara University, Istanbul, Turkey, in 2004, and his Ph.D. degree from the same institution in 2013. From 2015 to 2016, Dr. Yilmaz was a visiting scholar at the Smart Grid Research Center (SMERC) at the University of California, Los Angeles (UCLA). His primary research interests include smart grid technologies, renewable energy, machine learning, and signal processing. Dr. Yilmaz is a partner of the medical company Biosys LLC. He has served as Editor-in-Chief of the Balkan Journal of Electrical and Computer Engineering (BAJECE) and the European Journal of Technique (EJT). Additionally, he is the owner of INESEG, a publishing organization. Dr. Yilmaz has authored over 50 research articles, several book chapters, and frequently delivers invited keynote lectures at international conferences. He has also led his research team as the Principal Investigator in several European projects. He is an IEEE Senior Member.
Content
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.
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.