Artificial Intelligence

Department of Artificial Intelligence

Al-Saeed University

👁️ Program Vision

Leadership and local and regional excellence in the field of Artificial Intelligence education and research to meet the requirements of society and sustainable development.

🎯 Program Mission

To prepare highly competent and competitive graduates in the field of Artificial Intelligence who are capable of self-learning, providing effective smart technical solutions that address societal challenges, and adhering to professional ethics through an innovative and creative learning environment supported by active partnerships.

📌 Program Objectives

The program aims to:

Provide students with theoretical and practical concepts and knowledge in Artificial Intelligence, enabling them to compete effectively in the local and regional labor markets.
Equip students with skills to design, evaluate, and implement Artificial Intelligence solutions to solve real-world problems, meet market demands, and serve the community.
Empower students to work effectively in teams, pursue continuous self-learning, and communicate efficiently while adhering to professional ethics and demonstrating social responsibility.
Enhance students' scientific research capabilities, enabling them to pursue postgraduate studies and adapt to rapid technological advancements in Artificial Intelligence.

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Study Plan

You can view and download the detailed study plan and courses for the Artificial Intelligence Program.

Download / View Study Plan (PDF)

📖 Specialized Books and Scientific References

The list contains a collection of specialized books and scientific references in Artificial Intelligence and Machine Learning, available as printed copies for in-library reading and consultation:

Book Title	Authors	Year	Book Description
Machine Learning in Python: Essential Techniques for Predictive Analysis	Michael Bowles	2015	Explains how to use Python to build predictive models and analyze data, with a focus on machine learning algorithms, data preparation, model selection, performance measurement, and practical model implementation.
Machine Learning: A First Course for Engineers and Scientists	Andreas Lindholm; Niklas Wahlström; Fredrik Lindsten; Thomas B. Schön	2022	An academic introduction to machine learning within a statistical framework, beginning with fundamental methods such as k-NN, decision trees, and regression, and extending to neural networks, deep learning, clustering methods, and generative models, with attention to ethical aspects.
Computer Organization and Design: The Hardware/Software Interface – 5th Edition	David A. Patterson; John L. Hennessy	2013	A fundamental reference on computer organization that explains the relationship between hardware and software, processor architecture, assembly language, arithmetic, pipelines, memory hierarchy, input/output, and examples involving MIPS, ARM, and x86.
Data Structure and Algorithms Using C++: A Practical Implementation	Sachi Nandan Mohanty; Pabitra Kumar Tripathy	2021	Connects C++ programming fundamentals with data structures and algorithms through practical examples. It focuses on solution design, implementation and compilation steps, debugging, and efficient use of algorithms for solving real-world problems.
Software Engineering – 10th Edition	Ian Sommerville	2015	A comprehensive software engineering reference covering system development processes, agile and plan-driven methods, requirements engineering, design, implementation, testing, and maintenance, with emphasis on building reliable and maintainable software.
Natural Language Processing in Action: Understanding, Analyzing, and Generating Text with Python	Hobson Lane; Cole Howard; Hannes Max Hapke	2019	A practical guide to building natural language processing systems with Python. It covers text representation and analysis, processing pipelines, deep learning, neural networks, and tools such as Keras, TensorFlow, and scikit-learn, with practical examples.
Deep Learning: Foundations and Concepts	Christopher M. Bishop; Hugh Bishop	2023	Provides a comprehensive foundation for understanding core ideas in deep learning, including probability, neural networks, representation learning, convolutional networks, and generative models, in a style suitable for teaching, self-study, and research.
Artificial Intelligence: Foundations of Computational Agents – 3rd Edition	David L. Poole; Alan K. Mackworth	2023	A foundational artificial intelligence reference focused on computational agents and how to design and build them. It covers inference, search, planning, and learning, with updated content on deep learning, causality, and the social impacts of artificial intelligence.
Fundamentals of Machine Learning for Predictive Data Analytics – 2nd Edition	John D. Kelleher; Brian Mac Namee; Aoife D’Arcy	2020	Explains the predictive analytics lifecycle from data exploration to model building and evaluation, and presents learning approaches based on information, similarity, probability, and error, with practical examples and case studies.
Fundamentals of Internet of Things	Sudhir Kumar	2022	An introduction to Internet of Things networks, components, architecture, and applications, focusing on the foundations that enable connected systems to collect, share, and use data for automation, monitoring, homes, and industry.
Introduction to Machine Learning – 4th Edition	Ethem Alpaydın	2020	A comprehensive academic book introducing the principles and algorithms of machine learning from theoretical and practical perspectives, with important updates on deep learning and neural networks and applications such as speech recognition, translation, and intelligent systems.
Fundamentals of Network Security	John E. Canavan	2001	An introductory reference on network security explaining core security concepts, risk assessment, protection models, and information security, with practical topics such as intrusion detection systems, security policies, passwords, and security auditing.
A Guide to the Project Management Body of Knowledge (PMBOK® Guide) – Sixth Edition	Project Management Institute (PMI)	2017	A comprehensive standard reference for project management that defines knowledge areas and project management practices and covers planning, execution, monitoring and control, and closing, along with agile and adaptive approaches and the skills needed for project success.
Project Management: A Systems Approach to Planning, Scheduling, and Controlling – 12th Edition	Harold D. Kerzner	2017	A comprehensive project management book presenting a systematic approach to planning, scheduling, and controlling projects. It addresses stakeholders, governance, performance metrics, risk management, and traditional and agile approaches to projects.