The Bachelor of Science in Artificial Intelligence (BSAI) program aims to provide quality education in the field of artificial intelligence based on internationally recognized standards for undergraduate programs. The program seeks to produce AI professionals who can efficiently deploy AI technologies, implement solutions according to market and society needs, particularly in the UAE and Gulf region, and prepare individuals for lifelong learning and research.
The program educational objectives (Goals) of BSAI are as follows:
Graduates from the BSAI program will have the following characteristics within a few years of graduation:
PEO#1: Apply relevant knowledge, skills, and tools to develop and deliver AI solutions in professional and multidisciplinary environments; pursue further education or research or engage in entrepreneurial activities.
PEO#2: Function effectively as a team member or leader, actively addressing AI-related ethical and legal issues, and demonstrating awareness of the societal impact of AI.
PEO#3: Adapt and innovate AI technologies and engaging in lifelong learning and professional development.
Prof. Mohammed Al Betar (Profile)
Note: If Subject Proficiency EmSAT requirement is unmet, the following options will be accepted:
Equivalent qualifications from other educational systems are accepted. For more details, please check the Student Handbook. For further information, please refer to the university admissions policy.
Students will be eligible for the degree of BSAI after completing the following :
Graduates of the BSAI program will gain deep knowledge of AI technologies and methodologies, positioning them as significant contributors to the advancement of global industries. They will be equipped with the essential knowledge and skills to take on a variety of roles at both managerial and technical levels, such as AI engineer, machine learning specialist, data scientist, robotics developer, or intelligent systems architect. Graduates of the BSAI program may also choose to pursue postgraduate study and research.
The BSAI requires the completion of 120 credit hours of coursework. In addition, after completing 90 credit hours, the student must complete a 16-week internship program (at least 30 contact hours per week). This internship experience is equivalent to three credit hours, making the total completion requirement 123 credit hours.
On successful completion of this Program, the graduate will be able to:
PLO#1: Analyze a complex computing problem and apply principles of computing, artificial intelligence, and other relevant disciplines to identify the appropriate solution.
PLO#2: Design, implement, and evaluate a computing-based solution to fulfill specified computing requirements within the Artificial Intelligence discipline.
PLO#3: Communicate effectively in diverse professional settings, including technical reporting, client interactions, and interdisciplinary collaborations.
PLO#4: Recognize professional responsibilities and make informed judgments based on legal and ethical principles in the fields of computing and Artificial Intelligence.
PLO#5: Perform effectively as a member or leader within AI and multidisciplinary teams.
PLO#6: Integrate knowledge from other relevant disciplines with Artificial Intelligence theories, techniques, and tools to develop computing-based solutions that meet stakeholders' requirements.
PLO#7: Apply principles of innovation and entrepreneurship, integrate sustainability practices, and demonstrate continual learning and adaptability in the field of computing and Artificial Intelligence.
This course covers the essential mathematical topics that students specializing in information technology need. Topics covered are plane analytic geometry; matrices and determinants; solution of a system of linear equations; real functions limits, continuity, differentiation and applications; integration and applications; and graphing.
This course introduces Discrete Mathematics skills to Information Technology, Information Systems, and Computer Engineering students. These skills enhance their ability to both analyze and describe mathematically many of the algorithms and data structure performance characteristics. Topics covered include propositional logic, predicate logic, inference, mathematical induction & other proof techniques, counting, sets, functions, recursion, relations, graphs, and trees.
This course will cover the following topics: Linear Equations and Vectors, Matrix and Linear Transformation, Determinants, Eigenvalues and Eigenvectors, Vector Space and Subspace, and Orthogonality.
This course provides knowledge and skills in problem-solving and introductory programming using the Java programming language. Topics covered include the problem-solving process, data types, variables, constants, scope and memory locations, simple sequential programs, basic input/output, selection and repetition control structures, arrays and strings, and user-defined functions.
This course's primary objective is to introduce object-oriented programming concepts. Topics include reviewing the system and user-defined methods, classes and objects, arrays of objects, inheritance, polymorphism, association, aggregation, and composition. The course also covers recursive algorithms and exception handling. This course is not a comprehensive introduction to all Java concepts such as applets and socket programming.
This course introduces students to scientific computing and Artificial Intelligence concepts to solve data analytics problems. The primary focus is on structured data. Topics include control statements, functions, sequences, dictionaries, sets, arrays, strings, files and exceptions, and object-oriented concepts.
The course aims to introduce students to the concept of a modern data ecosystem. Students will learn how to perform the principal tasks of managing, extracting, transforming, and loading (ETL) data. Students will learn the different types of data modeling methods that are used for the three well-known data types: structured, semi-structured, and unstructured. This course also explains the data lifecycle in a data analytics project, starting from data collection (data importing, data crawling), preprocessing (cleaning and transformation), ingestion, and warehousing. The course also covers the elementary visualization aspects needed to understand and explore the data. By the end of this course, students will be able to perform all data engineering tasks required for any data analytics project.
The course covers concepts of program performance (time and space complexity), recursion, definitions, operations, and implementations of the list, stack, queue, tree, binary search tree, priority queue, heap, hash table, and graph data structures and their applications; sorting; and searching.
This course provides students with knowledge of how to program with two of the most important deep learning framworks in python: Tensorflow and Keras. The students will learn how to build, train and test deep learning models with TensorFlow and Keras. Topics covered in this course include important concepts of Tensorflow and keras framworks, loading and preprocessing data into Tensorflow, Encoding features for Deep learning Models, Build, Train and Test Deep learning models with Keras. Working ANN and MLP deep learning models with Keras will be given.
The aim of this course is to introduce students to the methods and algorithms of machine learning and in particular deep learning models for supervised and unsupervised type learning. Topics covered are: neural networks models for classifications and clustering problems, linear and logistic regression, support vector machines (SVM), probabilistic models, dimensionality reduction techniques, reinforced learning, ensemble learning, multiclass classifications, and model selection and evaluation. Students are also required to work on an individual project that embodies a machine-learning solution to a problem.
An introduction to the basic knowledge representation, problem-solving, and learning methods of artificial intelligence. Topics will include specific AI techniques, a range of application areas, and connections between AI and other areas of study (i.e., philosophy, psychology, sustainability). Techniques may include heuristic search, automated reasoning, machine learning, deliberative planning and behavior-based agent control. Application areas include robotics, games, knowledge representation, and natural language processing.
This course is designed to give a theoretical and practical background in database techniques. It covers database concepts, data models, data dictionary, entity-relationship (ER) and enhanced entity relationship (EER) diagrams, and the relational data model, converting an E-R model to a relational model, Structured Query Language (SQL), normalization, and physical database design. Oracle software is used in the Lab.
This course covers the principles and concepts of modern operating systems. Topics include operating system services; operating systems structures; operating system processes: threads, synchronization, CPU scheduling, deadlocks; memory management: main memory, virtual memory; storage management: storage structures, file-system interface, and file-system implementation; operating System protection and security; and virtualization.
This course will introduce students to the concepts of Deep Learning (DL) and Reinforcement Learning (RL) and their key principles. The course covers feed-forward neural networks, convolutional neural networks, recurrent neural networks, sequence modeling, techniques to improve neural networks, reinforcement learning methods, and addressing ethical and societal issues.
This course will cover topics in evolutionary computation and their application to solving optimization problems. Course topics include the basics of Genetic Algorithms, Constrained Optimization, Multiobjective Optimization, Swarm Intelligence, Genetic Programming, Coevolutionary Systems, and Combinatorial Optimization. By examining representation, mutation, recombination, fitness, and selection, participants will learn how to design, implement, and analyze evolutionary algorithms to effectively address real-world challenges across various domains.
The internship familiarizes students with actual working environments. It allows students to integrate their knowledge and skills by applying them to real-world problems encountered in business and industry. The Internship also provides the student with insight into working on actual AI problems, helps develop communication and teamwork skills, and addresses ethical and professional issues applicable to computing practices.
This is an introduction to the technologies that are used for big data. The aim of the course is to provide students with the knowledge required to use big data technologies and learn how to store, and process big data sets. Topics covered include: Big Data and Hadoop, Hadoop Distributed File System, Map Reduce, PIG, HIVE, HBase, NoSQL, NewSQL, PySpark, and Spark.
Introduction to computer networks and the Internet: Components of data communication, data flow, data communication system, network criteria, types of connections, topologies, transmission media, parallel and serial transmission, network types, protocol and standards, protocol layers, and the OSI model. Physical layer: Data and Signals, Nyquist Bit Rate, and Shannon Capacity. Data Link Layer: Error detection and correction, multiple access, MAC addressing, switches, ARP, MAC Frame (IEEE 802.3 protocol), Wired LAN Ethernet, and WLAN (IEEE 802.11 protocol). Network Layer: Network Devices, Virtual circuits, routers, IP Addresses, subnetting, IP protocols and routing algorithms, NAT, IP header format, ARP, and DHCP. Transport layer: UDP, TCP, and congestion control. Application layer: HTTP, FTP, SMTP, POP3, DNS, and peer-to-peer applications.
Natural language processing (NLP) is a crucial part of artificial intelligence (AI), modeling how people share information. This course explores current statistical and deep learning techniques for the automatic analysis and generation of natural (human) language data. Topics from traditional NLP include language modeling, word-sense disambiguation, morphological analysis, part-of-speech tagging, syntactic parsing, semantic interpretation, co-reference resolution, and discourse analysis. Techniques include statistical and deep learning techniques such as n-gram models, Recurrent Neural Networks, attention mechanisms, Transformers, and pretraining. NLP applications covered include information extraction, question answering, speech recognition, interactive dialog systems, machine translation, sentiment analysis, and summarization.
This course aims at introducing fundamental security concepts to students. Main security threats and related countermeasures are presented. Students will learn the importance of protecting information stored on computer systems from unauthorized access. Topics covered include cryptography, authentication, access control, database security, malicious software, denial of service, network security, security management and risk assessment, security controls, plans and procedures, and legal and ethical aspects of information security.
This course will introduce students to the principles of Machine Learning Operations, which is the process of automating and managing the end-to-end machine learning lifecycle. The course will cover topics such as data preparation, model training and deployment, monitoring, and optimization, the tools and techniques used in MLOps as well as ethical and sustainability considerations. By the end of this course, the students will be ready to employ their new production-ready skills to participate in developing leading-edge AI technology to solve real-world problems.
This course provides a comprehensive introduction to computer vision and pattern recognition. It is, therefore, primarily concerned with the problem of capturing and making sense of digital images. The field draws heavily on many major subjects, including digital image processing, artificial intelligence, feature extraction and selection, image classification and recognition, and scene understanding. It also discusses various emerging applications of deep learning in computer vision and pattern recognition, such as: Attention and transformers, Object detection and recognition, Image segmentation, image/video classification, Generative models (GANs, VAEs, autoregressive), and object tracking.
This course aims to introduce students to the theory and practice of cloud computing. Topics include parallel and distributed systems; deployment and service models; cloud infrastructure; applications and paradigms; resource virtualization; resources management and scheduling; networking support; cloud storage systems; cloud security, and sustainability in a cloud environment.
This course offers a comprehensive introduction to the Internet of Things (IoT), a cutting-edge technology where everyday objects are equipped with devices that enable them to exchange data. Participants will explore foundational principles and the networking protocols essential for IoT implementations. Key subjects include the architecture of IoT systems, the integration of intelligent objects, methods of sensing and actuation, information display techniques, Wireless Personal Area Networks (WPANs), Wireless Body Area Networks (WBANs), cloud solutions for IoT, as well as considerations for security, privacy, robustness, and reliability in IoT environments. Additionally, the course will delve into various applications of IoT technology.
This course will examine the ethical issues that arise in using computers and the responsibilities of those who work with computers, either as computer science professionals or as end users. Topics covered include legal, social and ethical issues surrounding computer and AI technology and its use; privacy; intellectual property rights and copyright laws; information technology and AI codes of ethics; issues of privacy and confidentiality in computing and AI systems; risks of using computers and AI systems; and computer crime: computer viruses, hacking, phishing & pharming, and scams.
This course covers the core areas of robotics and AI, focusing on the design, operation, and ethical considerations of autonomous systems. Key topics include the fundamentals of robot components, automation and autonomy, software architectures, behaviors, perception, locomotion, sensing, and learning algorithms such as supervised, unsupervised, and reinforcement learning. The curriculum also explores advanced areas like multirobot systems (MRS), human-robot interaction, and the ethical implications of deploying intelligent robots, including discussions on Asimov's Laws and responsible robotics. Through a combination of theoretical understanding and practical applications, students will examine navigation techniques, metric and topological path planning, and the integration of IoT and cloud computing in robotics, preparing them to design, evaluate, and ethically implement intelligent robotic systems.
The graduation project provides a unique opportunity for students in the Artificial Intelligence program to apply their knowledge of the foundations, theory, and methods of AI and software development to address and provide solutions (i.e., developing software applications) to problems in industry, government and the non-profit sector and other areas. Typically, two to four students work together on each graduation project. A faculty mentor supervises each team, and projects typically progress through several phases. This course includes the first stage of the graduation project, where the student(s) should define, analyze the problem, and finally write a proposal. Then, it will be presented to a predetermined committee in the department. It includes weekly meetings with the supervisors.
This is the second stage of the graduation project, which includes the practical aspects: design, implementation, and testing of the project specification developed in capstone project I. Students work as a team in developing the specification into a real-world working application drawing on their internship experience. Team members are evaluated by the project supervisor and a committee in the midterm in week 6 of the term and at the end of the term. Team members are required to meet weekly with the supervisor.
This course introduces semantic web technologies and their applications. Students will learn different aspects of web semantic representation and how to reason about data using ontologies. The course will introduce existing technologies and explain how to apply semantic web technologies to current and potential real-world applications. Topics include the W3C's semantic web activity, semantic modeling and ontology representation, semantic web applications, logic for the semantic web, ontologies modeling and design using Resource Description Framework (RDF) and Web Ontology Language (OWL), and semantic web applications.
This course will introduce students to techniques for extracting and analyzing large-scale network data and how to reason about it. Students will also learn about social network structure, dynamics, and evolution. Topics include methods for social network analysis, network models, graph representation, graph traversal algorithms, graph mining, link analysis and network community detection, information propagation on the web, recommendation systems, integration and analysis of big data, and advanced computational techniques for social networks. Students will also learn to use social network analysis packages/tools.
This course introduces business intelligence (BI) concepts. It explores how business problems can be solved effectively by using operational data to create data warehouses to store data, apply big data analytics, and use data mining tools and analytics to gain new insights into organizational operations. In particular, students will learn effective modeling techniques (dimensional modeling), the foundations and technologies for the decision-making process, the ETL process, Business Performance Management, an overview of Data Mining, analytical modeling (descriptive, predictive, and prescriptive), and some of the Emerging Technologies and Trends in business intelligence.
This course will introduce students to various approaches for building recommender systems. Topics are collaborative, content-based, knowledge-based, hybrid algorithms, filtering techniques, dimension reduction techniques for the user-product preference space, candidate generation, scoring, re-ranking, and evaluation and metrics for recommender systems, and other related topics to recommender systems. Students will implement recommendation algorithms using an open-source toolkit and conduct experimental assessments.
This course explores the role of emerging AI algorithms and techniques in the financial, law, and industrial sectors. It introduces the basics of AI and its impact on financial, law, and industrial sector services. The course gives an overview of the potential implementations of AI within an organization. It also explores a deep dive into determining whether a business is prepared for the future using several AI techniques. Additionally, the course provides hands-on forecasting experience in different sectors, using several AI and machine learning techniques to apply forecasting techniques to specific real-world data.
Introduction to unmanned aerial systems (UAS) including drones and autonomous unmanned aerial vehicles (UAV) with sensors. It aims to introduce students to the fundamental principles of autonomous systems, focusing on unmanned aerial vehicles (UAVs), commonly known as drones. It covers robotic systems' design, control, perception, navigation, mapping, planning, localization, and decision-making processes. The applications of machine learning and swarm intelligence, communication, and integration are other drone topics. The Ethics, legal, safety regulations, and risk management issues related to the drones are also considered.
This course aims to introduce students to new developments in AI that are not specifically covered in the curriculum and in which a faculty member has developed interest and proficiency. The intention is to provide a rapid response to current trends and to widen students’ knowledge in various areas of AI. The specific content of the course will depend on the particular area selected at the time.
This course provides students with the necessary knowledge and skills needed to design and implement interactive computer systems using the latest human computer interaction (HCI) principles and theories from AI perspective. Topics covered by this course include, general overview of HCI, universal usability and sustainability, guidelines, principles, and theories of HCI, managing design processes, evaluating design, Interaction styles, available AI approaches utilization for HCI, critical design issues, search and visualization.
The course focuses on designing for Extended Reality (XR), which encompasses Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). Topics include metaverse concepts, integrating virtual intelligence into XR spaces to create dynamic user interfaces and personalized experiences; user experience design principles specific to XR; an in-depth exploration of technical aspects of XR, such as advanced 3-D modeling, real-time rendering techniques, and integration of XR with emerging technologies; the design and evaluation of user interfaces and related guidelines, standards, and specification languages before introducing XR concepts and technologies, covering tools in AR, VR and MR, and related topics; the future of XR and its integration with emerging technologies like IoT and blockchain; ethical issues of XR; and engagement with industry partners.