Bachelor of Science in

Course description: Clear communication is one of the most essential and undervalued skills in the tech industry. No matter how innovative a project is, it won’t gain support, funding, or traction unless it’s explained in a way that people understand and care about. This course gives students the tools to bridge that gap. It teaches them how to translate complex technical ideas into clear, persuasive stories that resonate with investors, teammates, clients, and the public. In an industry where success depends as much on communication as code, these skills are not optional—they’re fundamental. Students will learn how to craft narratives that make their work meaningful and memorable, whether presenting live, writing professionally, or building a digital presence. The course combines practical tools with creative thinking: from redesigning academic slides to delivering TED-style talks, from writing concise executive summaries to launching social media campaigns. By the end of the course, students will have a portfolio of high-quality work and the confidence to communicate ideas that matter. 

Course description: The Founder’s Playbook course provides first-year students with a hands-on journey through the entrepreneurial process, from ideation to launch. Through experiential learning, students will discover how to identify real problems, build solutions, validate ideas with customers, create prototypes, and pitch their ventures. The course emphasizes learning-by-doing, where students form teams and work on actual startup ideas throughout the semester. Integrating AI tools throughout the journey, students will learn modern approaches to building ventures in the digital age. By the end of the course, students will have experienced the complete startup lifecycle, developed an entrepreneurial mindset, and gained practical skills to transform ideas into viable ventures. No prior business knowledge is required – just curiosity and willingness to embrace the entrepreneurial journey. 

Course description: This course provides students with a comprehensive understanding of the United Arab Emirates’ (UAE) history, culture, society, and political system. It explores the UAE’s transformation from a traditional society to a modern, globally connected nation. Topics include the UAE’s governance, economic development, cultural heritage, and role in regional and international affairs. The course also examines the values of tolerance, sustainability, and innovation that underpin the UAE’s vision for the future. Through lectures, discussions, and projects, students will gain a deeper appreciation of the UAE’s unique identity and its contributions to the global community. 

Course description: This course explores fundamental questions about existence, knowledge, morality, and human nature. Students will engage with key philosophical traditions and ethical theories, analyzing concepts such as justice, rights, and the nature of good. The course also examines the ethical implications of artificial intelligence, including issues of autonomy, privacy, and bias in AI systems. Through discussion and critical thinking, students will consider how AI challenges traditional ethical frameworks and shapes modern philosophy. 

Course description: This course focuses on the principles and practices of data privacy and security in the context of AI and data-driven systems. Students will learn about encryption, access control, anonymization, and secure data storage. The course also covers regulatory requirements such as GDPR and CCPA, as well as ethical considerations for protecting sensitive data. Practical exercises will include designing secure AI systems and responding to data breaches. 

Course description: This course explores the legal, ethical, and societal implications of artificial intelligence in business contexts. Students will examine regulatory frameworks, ethical dilemmas, and accountability issues surrounding AI deployment. Topics include bias in AI, intellectual property, data protection laws, and the ethical use of AI in decision-making. Case studies and real-world scenarios will be used to analyze the challenges and responsibilities of implementing AI in business. 

Course description: This course provides an in-depth exploration of the ethical and societal dimensions of Artificial Intelligence (AI), aiming to equip students with the skills to make responsible decisions when developing, deploying, and interacting with AI systems. It covers the core principles of AI ethics, including fairness, transparency, accountability, and privacy. The course also examines the broader societal impacts of AI, such as its effects on employment, social inequality, and public policy. Through a combination of theoretical frameworks and practical case studies, students will learn how to critically assess and address the ethical dilemmas posed by AI technologies. 

Course description: This course introduces students to foundational principles of microeconomics and macroeconomics, with a special emphasis on how modern technologies, particularly artificial intelligence (AI), are reshaping economic landscapes. Students will explore key concepts such as supply and demand, market equilibrium, consumer and producer behavior, market structures, and macroeconomic indicators including gross domestic product (GDP), inflation, and unemployment. Through lectures, case studies, and applied lab sessions, students will learn to evaluate the role of AI in economic decision-making, labor markets, and predictive analytics. By the end of the course, students will be able to apply economic reasoning to real-world scenarios and critically assess their economic implications.

Course description: This course provides a foundational understanding of political systems, governance, political theory, and public policy. Students will explore key concepts such as power, democracy, and political behavior, along with the structure of governments and international relations. Consideration is given to how AI is reshaping political campaigns, governance, and public opinion in the digital age. 

Course description: This course introduces students to the tools and methods used to evaluate and formulate public policies. Topics include policy design, impact assessment, and decision-making processes. The course explores the growing role of AI in public policy, particularly in data analysis, predictive modeling, and automated decision-making. Students will examine how AI technologies are transforming policy analysis, enhancing the ability to predict outcomes and optimize solutions for complex societal challenges. 

Course description: This course explores the principles of human cognition, including perception, memory, learning, and decision-making. Students will examine how these cognitive processes shape behavior and mental function. The course also compares human cognition with AI systems, highlighting differences in learning, problem-solving, and information processing. By analyzing the strengths and limitations of both, students will gain insights into how AI mimics, complements, and contrasts with human cognitive abilities in various contexts. 

Course description: This course examines the role of AI in the development of smart cities. Topics include urban data analytics, intelligent transportation systems, energy management, and public safety. Students will learn how AI technologies can be leveraged to improve urban living conditions and sustainability. Case studies and real-world projects will be used to illustrate the application of AI in smart city contexts. 

Course description: This course examines the transformative role of artificial intelligence in scientific research and discovery. Students will explore how AI is being used to accelerate research in fields such as physics, chemistry, biology, and environmental science. The course will cover topics such as AI-driven data analysis, simulation and modeling, and the use of AI in hypothesis generation and experimental design. Students will also discuss the ethical implications of AI in scientific research and the potential for AI to democratize access to scientific knowledge. 

Course description: This course explores the intersection of technology and the humanities, focusing on how digital and artificial intelligence tools and methods can enhance research in fields like literature, history, and philosophy. Students will learn to analyze and visualize textual, historical, and cultural data using digital technologies. The course includes natural language processing, machine learning, and data mining to uncover new insights and patterns in human culture and historical records. 

Course description: Linear algebra and calculus represent some of the most mature and structured branches of mathematics and have been widely applied across the sciences and engineering. This course provides a foundational introduction to both areas, with an emphasis on core concepts, methods, their geometric and analytical interpretations and their applications in the domain of AI. Topics include vector spaces, linear transformations, systems of linear equations, eigenvalues, continuity, differentiation, integration, and their applications. The course is designed to equip students with essential mathematical tools for the advanced courses on calculus and linear algebra and for further study in artificial intelligence, data science, modeling, and beyond. 

Course description: Probability and Statistics covers foundational concepts in probability, statistical analysis, and data modeling with a focus on applications in artificial intelligence. Students will learn to apply probabilistic reasoning, hypothesis testing, regression analysis, and data distributions to real-world AI problems. The course emphasizes the role of statistics in machine learning, predictive modeling, and decision-making processes. Through practical examples and hands-on exercises, students will develop the skills needed to analyze and interpret data in AI contexts. 

Course description: The course defines artificial intelligence and present examples of AI applications to provide historical context for the field’s evolution. It then delves into foundational concepts, including search algorithms, logic, and introduction to basic methods of machine learning, computer vision, and natural language processing. The course wraps up with an exploration of the ethical implications of AI and a look at potential future advancements. Hands-on exercises will allow students to apply basic AI techniques, select appropriate technologies for specific problems, and consider design implications. Assessment will be via a data challenge long project and oral exams. 

Course description: This course provides the broader context of computing and systems programming to form the bridge from AI1030 to ENG1010 and to higher-level elective courses such as operating systems, programming languages, and compilers. Independent of preparation for these courses, students should come away from the course with an understanding of how computers and networks work, the theoretical principles that they’re built on, and how these aspects intersect to shape performant software design. 

Course description: The course is designed to provide a solid foundation in Python, one of the most widely used programming languages in the AI landscape. The course will cover the basics of Python syntax, data types, variables, control structures (such as loops and conditionals), functions, and classes, enabling students to write simple yet effective programs. Moreover, students will learn the most useful libraries that implement important features like regular expressions, data structures, text processing algorithms, and graph algorithms. Emphasis is put on activity-led learning, with practical exercises and projects that help students apply concepts in real-world scenarios. By the end of the course, students will have a strong understanding of Python’s core principles and be equipped with the necessary skills to develop small-scale applications and tackle more advanced topics, such as data analysis, machine learning, deep learning, web development, or automation. 

Prerequisite course/s: AI1030 Python Programming 

Co-requisite/s: AI1120 Probability and Statistics

Course description: This introductory course provides a foundational understanding of the key concepts, techniques, and applications of machine learning (ML). Designed for beginners, the course covers the basics of supervised, unsupervised, and reinforcement learning, along with an overview of common algorithms, data preprocessing/exploration and model evaluation. Through hands-on exercises and real-world examples, students will gain practical experience in building and deploying simple ML models. By the end of the course, learners will have a solid grasp of the principles driving ML technologies and their transformative impact across industries. 

Course description: This course introduces students to the fundamentals of software and web development, focusing on designing, building, and deploying applications. Students will learn programming languages, frameworks, and tools essential for creating dynamic and interactive web applications, with an emphasis on integrating AI functionalities. 

Prerequisite course/s: AI1215 Machine Learning 

Course description: This course provides a comprehensive foundation for understanding the core deep learning (DL) concepts, techniques, and applications. DL is an offspring of machine learning that leverages neural networks to model complex patterns in data. Designed for beginners, the course covers essential topics such as neural network architectures, activation functions, backpropagation, and optimization algorithms, while also introducing popular frameworks like PyTorch. This course serves as an important prerequisite for further exploration into advanced topics in artificial intelligence and its applications. 

Prerequisite course/s: AI1030 Python Programming, MTH1110 Calculus amd Linear Algebra

Course description: This course covers the design, analysis, and implementation of fundamental algorithms and data structures, focusing on efficiency and problem-solving techniques.

Course description: This course introduces the principles and practices of DevOps and cloud services, focusing on their application in AI projects. Students will learn about continuous integration/continuous deployment (CI/CD), infrastructure as code (IaC), and cloud service providers like AWS, Azure, and Google Cloud. The course also covers containerization, orchestration, and monitoring of AI applications.

Prerequisite course/s: AI1215 Machine Learning

Course description: This introductory course to natural language processing (NLP) and information retrieval (IR) provides a foundational understanding of how computers process, analyze, and retrieve human language data. Students will explore key concepts such as tokenization, document indexing/ranking/classification, document indexing and ranking, and word and document embedding methods, while gaining hands-on experience with basic algorithms and tools used in NLP and IR. The course also covers practical applications, including search engines, chatbots, and text classification systems.

Course description: This introductory course on generative AI and foundation models provides a comprehensive overview of the methods and techniques driving modern AI systems capable of generating data such as text, images, sounds, graphs and other data types. Participants will explore the core principles behind generative models including advanced transformer architectures, diffusion models, and autoregressive techniques, as well as their applications in natural language processing, computer vision, and beyond. The course emphasizes hands-on learning, enabling students to understand and implement state-of-the-art methods while critically evaluating their ethical implications and limitations. This course is ideal for those seeking to harness the power of generative AI while understanding its methodological and ethical dimensions.

Prerequisite course/s: AI1215 Machine Learning

Course description: This course introduces students to the fundamentals of data science and data mining, focusing on techniques for extracting meaningful patterns and insights from large datasets. Topics include data preprocessing, clustering, classification, association rule mining, and anomaly detection. Students will gain hands-on experience using tools like Python, R, and SQL to analyze real-world datasets and solve complex problems.

Course description: This course covers core project management principles with a focus on AI-driven projects. Students will learn how to apply traditional and agile methodologies to plan, execute, and monitor projects involving AI technologies. The course explores AI-specific challenges, such as data management, ethical concerns, and team dynamics, while introducing AI tools for decision-making, performance tracking, and risk management. Students will gain practical skills for leading successful AI-related projects in various industries.

Course description: This course focuses on the creation and manipulation of multimedia content, including graphics, audio, and video. Students will learn tools and techniques for designing engaging multimedia experiences, with an emphasis on AI-driven content generation and enhancement.

Course description: Human-computer interaction (HCI) is the study of how people interact with computers and how to design technology that is both user-friendly and effective. This course explores the principles, methods, and techniques used to design, implement, and evaluate interactive systems. Students will learn about user-centered design, usability testing, and the latest trends in HCI, including virtual reality, augmented reality, and mobile interfaces. The course combines theoretical foundations with practical applications, enabling students to design intuitive and engaging user experiences.

Course description: This course focuses on the generation and utilization of synthetic data using AI techniques. Students will learn about generative models such as GANs (generative adversarial networks) and VAEs (variational autoencoders), and their applications in creating realistic datasets. The course also covers the ethical considerations and potential biases in AI-generated data.

Prerequisite course/s: AI1030 Python Programming

Course description: The course provides a foundational understanding of how data is organized, stored, searched and managed in digital systems. Students will use frameworks such as Pandas and SQLAlchemy, to efficiently handle, analyze, and manipulate data. Students will learn how to work with Pandas data structures like DataFrames and Series to clean, transform, and analyze datasets, as well as how to connect Python to databases for seamless data integration. The course also covers essential database concepts, including relational models, SQL queries, and data normalization.

Prerequisite course/s: AI1030 Python Programming

Course description: This course provides a comprehensive introduction to web scraping and data mining techniques. Students will learn how to extract, clean, and analyze data from the web using various tools and libraries. The course covers data mining algorithms, data preprocessing, and the ethical considerations of web scraping. Practical applications in AI and machine learning contexts are emphasized.

Prerequisite course/s: AI1215 Machine Learning

Course description: This course introduces students to the practical use of AI tools and the art of prompt engineering, enabling them to effectively interact with and leverage generative AI models like ChatGPT, DALL·E, and other large language models (LLMs). Students will learn how to craft precise prompts, optimize AI outputs, and apply these tools across various domains, including content creation, problem-solving, and automation.

Prerequisite course/s: AI1020 Computers and the Web

Course description: This course covers the principles of data communication and computer networks, including protocols, architectures, and security. Students will explore how data is transmitted, routed, and secured across networks, with a focus on supporting AI-driven applications and internet of things (IoT) systems.

Prerequisite course/s: ENG2030 Generative AI

Course description: This course explores the transition from large language models (LLMs) to functional chatbots, covering the methods and engineering practices required to build, deploy, and maintain conversational AI systems. Students will learn about natural language processing (NLP), dialogue management, and integration of LLMs into chatbot frameworks. The course also covers ethical considerations and performance optimization.

Prerequisite course/s: MTH1120 Probability and Statistics

Course description: This course teaches students how to effectively visualize and interpret data using graphical tools and techniques. Emphasis is placed on creating visual representations of complex datasets, including those generated by AI systems, to support decision-making and storytelling.

Prerequisite course/s: AI1250 Deep Learning and Neural Networks

Course description: This course focuses on the application of artificial intelligence in the healthcare sector. Students will learn how AI is being used to improve patient outcomes, streamline healthcare operations, and advance medical research. Topics include AI-driven diagnostics, personalized medicine, predictive analytics for patient care, and the use of AI in drug discovery. The course will also address the ethical considerations and regulatory challenges associated with AI in healthcare.

Prerequisite course/s: ENG1010 Algorithms and Data Structures

Course description: This is an introductory course that provides a comprehensive overview of the fundamental concepts, systems, and applications of robotics, including robot design, programming, control systems, and sensing. Students will explore the various types of robots, their components, and their applications in industrial and non-industrial settings. The course also delves into the latest advancements in robotics, such as robotic planning, dynamic control, and human-robot interaction.

Prerequisite course/s: ENG2020 Natural Language Processing

Course description: This course delves into the technologies and algorithms behind speech recognition and synthesis. Students will explore acoustic modeling, language modeling, and speech signal processing. The course also covers the development of speech-enabled applications and the integration of speech technologies into AI systems. Practical labs and projects will reinforce theoretical concepts.

Prerequisite course/s: AI1215 Machine Learning

Course description: This course introduces students to the principles and algorithms of reinforcement learning (RL), a subfield of artificial intelligence focused on training agents to make sequential decisions in dynamic environments. Topics include Markov Decision Processes (MDPs), value functions, policy optimization, Q-learning, deep reinforcement learning, and applications in robotics, gaming, and autonomous systems.

Prerequisite course/s: ENG2030 Generative AI

Course description: This course focuses on generative AI models that can process and generate data across multiple modalities, such as text, images, audio, and video. Students will explore architectures like transformers, GANs, and diffusion models, and learn how to build systems capable of tasks like text-to-image generation, video synthesis, and cross-modal retrieval.

Prerequisite course/s: ENG3010 Data Science

Course description: This course provides an in-depth understanding of big data technologies and their role in AI systems. Students will learn about data collection, storage, processing, and analysis techniques for large-scale datasets. Topics include distributed computing frameworks (e.g., Spark), data mining, and machine learning on big data. The course also covers challenges such as scalability, data quality, and real-time processing.

Course description: This course explores the intersection of computer vision, 3D graphics, and mixed reality (MR). Topics include 3D reconstruction, depth sensing, augmented reality (AR), virtual reality (VR), and the integration of AI with MR technologies for applications in gaming, healthcare, and education.

Course description: This course introduces students to foundational principles of microeconomics and macroeconomics, with a special emphasis on how modern technologies, particularly artificial intelligence (AI), are reshaping economic landscapes. Students will explore key concepts such as supply and demand, market equilibrium, consumer and producer behavior, market structures, and macroeconomic indicators including gross domestic product (GDP), inflation, and unemployment. Through lectures, case studies, and applied lab sessions, students will learn to evaluate the role of AI in economic decision-making, labor markets, and predictive analytics. By the end of the course, students will be able to apply economic reasoning to real-world scenarios and critically assess their economic implications. 

Prerequisite course/s: BUS1010 Economics 

Course description: This course covers the basic principles of financial and managerial accounting, including the preparation of financial statements, budgeting, and cost analysis. Students will learn how to record, classify, and interpret financial transactions. The course also explores the integration of AI technologies in accounting, focusing on automation in data entry, financial analysis, and fraud detection. Through practical exercises, students will understand how AI is transforming accounting practices for greater efficiency and accuracy. 

Course description: This course explores essential principles of effective teamwork, leadership styles, communication, and conflict resolution. Students will learn strategies for building collaborative, high-performing teams and developing leadership skills across diverse environments. The course also examines the role of AI in enhancing team dynamics, decision-making, and leadership processes, including the use of AI tools for team management, performance analysis, and problem-solving. Practical case studies and interactive activities will prepare students for leadership in a technology-driven world. 

Course description: This course explores the use of AI in marketing and social media, including customer segmentation, sentiment analysis, personalized recommendations, and influencer marketing. Students will learn how to leverage AI tools to analyze data and optimize online marketing strategies for social media. 

Course description: This course covers the end-to-end process of designing and developing AI-powered products. Students will learn about user-centered design, prototyping, agile development, and integrating AI technologies into products. Case studies of successful AI-driven products will be analyzed.

Course description: This course provides a comprehensive introduction to the principles, strategies, and tools of e-commerce and digital marketing in the modern business environment. Students will explore the dynamics of online business models, digital marketing channels, and the integration of technology to drive customer engagement and sales. Topics include e-commerce platforms, search engine optimization (SEO), social media marketing, content marketing, email marketing, analytics, and the legal and ethical considerations of digital business. Through case studies, hands-on projects, and real-world applications, students will develop the skills necessary to design, implement, and manage effective e-commerce and digital marketing campaigns.

Course description: This course introduces students to the principles and practices of design thinking and creativity as applied to business innovation. Students will learn how to use human-centered design methodologies to solve complex business problems, foster innovation, and create value for customers. Through hands-on projects, case studies, and collaborative activities, students will develop skills in empathy, ideation, prototyping, and testing. The course emphasizes the importance of creativity, critical thinking, and iterative problem-solving in driving business success in a rapidly changing global economy.

Course description: This course explores the application of artificial intelligence in various business contexts through the analysis of real-world case studies. Students will examine how AI technologies are being used to drive innovation, optimize operations, and create competitive advantages across different industries. The course will cover key concepts such as machine learning, natural language processing, and predictive analytics, and how these technologies are integrated into business strategies. Students will also learn to evaluate the ethical implications and societal impact of AI in business. 

Prerequisite course/s: BUS1015 Finance and Accounting 

Course description: This course explores the integration of data-driven decision-making and strategic planning in modern organizations. Students will learn advanced techniques in data analysis, forecasting, and performance metrics to drive business success. The course explores how AI technologies, such as machine learning and predictive analytics, enhance strategic management by optimizing operations, identifying market trends, and informing competitive strategies. Through case studies, students will apply these tools to real-world business challenges. 

Prerequisite course/s: BUS1015 Finance and Accounting: Mastering Business Finance, ENG3010 Data Science from Numbers to Knowledge

Course description: This course explores the intersection of artificial intelligence (AI) and financial technology (fintech), focusing on how AI is transforming the financial services industry. Students will learn about AI-driven applications such as algorithmic trading, fraud detection, credit scoring, robo-advisors, and blockchain integration. The course also covers the ethical, regulatory, and societal implications of AI in finance.

Course description: This course examines how AI systems can collaborate with humans to enhance productivity and decision-making in the workplace. Topics include human-AI interaction design, explainable AI, trust in AI systems, and the ethical implications of AI in the workforce.

Prerequisite course/s: BUS3015 AI Business Case Studies: Insights, Best Practices, and Market Impact

Course description: This course explores the application of artificial intelligence techniques to optimize supply chain operations. Students will learn how to use machine learning, optimization algorithms, and predictive analytics to improve demand forecasting, inventory management, logistics, and production planning.

Course description: This internship provides students with hands-on experience in applying AI technologies in real-world settings. Students will work with industry partners or research labs to solve practical problems, gain professional skills, and explore career opportunities in AI. 

Course description: Experience real world industry of academic experience working at a company, startup, or research lab. Full-time employment placement.  

Prerequisite course/s: AI4990 Co-op Experience 1: Learning by Doing in the Real World

Course description: Experience real–world industry of academic experience working at a company, startup, or research lab. Full-time employment placement.