Comprehensive Exam Track: Total Credit Hours Required to Finish the Degree ( 36 Credit Hours ) as Follows
Specialization Requirements
Students must pass all of the following courses
|
Course Number |
Course Name |
Weekly Hours |
Cr. Hrs. |
Prerequisite |
||
|---|---|---|---|---|---|---|
|
Theoretical |
Practical |
|||||
| 152696000 | RESEARCH METHODS | The course examines the role of business research and provides an overview of commonly used qualitative and quantitative research methods. Topics covered include scientific inquiry, the research process, proposal development, research design, hypothesis testing, primary and secondary data collection, statistical data analysis and presentation of research reports. | 3 | - | 3 |
- |
| 152696010 | INTRODUCTION TO DATA SCIENCE | This course is an introduction to the emerging field of data science, a field that combines business strategy, information technology and modeling methods. Topics covered include the evolution of data science, the benefits and opportunities of data science and its relation to machine learning and data infrastructure uses. Special emphasis will be placed on practical techniques for analyzing data including the use of statistical modeling as a data tool. | 3 | - | 3 |
- |
| 152696020 | DATA MANAGEMENT | This course is introduction to the concepts and theories of database management systems. Topics covered include data models, data integration and cleaning, database programming for extract, transform and load (ETL) operations, query optimization and database design. Special emphasis will be placed on query languages and search specifications, including Structured Query Language (SQL) for effective responses to user queries. | 3 | - | 3 |
- |
| 152696030 | DATA VISUALIZATION | This course is introduction to the theory and concepts of data visualization and the techniques used to create visual representation of large amounts of data. Topics covered include data representation, data and task abstraction, validation, tables and spatial data and maps and other channels. The emphasis will be on developing data visualization from a variety of sources and effectively communicating results. | 3 | - | 3 |
- |
| 152696040 | MACHINE LEARNING | This course provides an overview of machine learning and its growing role in the field of artificial intelligence. Students will examine different methods for machine learning and analyze how, why and when certain methods succeed. Topics covered include logistic regression, Bayesian Learning, tree-based models, neural networks, recommendation engines, and unsupervised and reinforcement learning. The emphasis will be on using statistical methods to build algorithms for machine learning. | 3 | - | 3 |
- |
| 152696050 | FOUNDATION OF AI | is course provides an overview of the field of artificial intelligence and its core techniques and applications. Topics covered include logic, constraint satisfaction, search, game playing, Markov decision processes and reasoning, planning and learning with certainty and uncertainty. Special emphasis will be placed on machine learning and its applications to real-world challenges. | 3 | - | 3 |
- |
| 152696060 | ADVANCED MACHINE LEARNING TECHNIQUES | This course is designed for students who have a foundational understanding of machine learning and wish to learn more about complex models and state-of-the-art techniques. The curriculum covers a broad spectrum of advanced machine learning methodologies, including ensemble methods, deep learning architectures, reinforcement learning, and transfer learning. Students will explore the theoretical underpinnings of these techniques, as well as their practical applications in solving real-world problems. | 3 | - | 3 |
- |
| 152696070 | DEEP LEARNING AND NEURAL NETWORK | This course" offers an intensive exploration into the realm of deep learning, focusing on the concepts, architectures, and applications of neural networks. This course aims to equip students with a deep understanding of how neural networks function, how they can be trained to recognize patterns and make predictions, and how they are applied to solve complex problems in areas such as image recognition, natural language processing, and more. | 3 | - | 3 |
- |
| 152696080 | NATURAL LANGUAGE PROCESSING | This course provides an overview of the ways that computers can process interpret written and spoken language and the role of natural language processing in artificial learning. Topics covered include processing text, classifying text, analyzing sentence structure and meaning, information extraction speech recognition and sentiment analysis. Emphasis will be placed on recent developments in computational linguistics and machine learning. | 3 | - | 3 |
- |
| 152696090 | COMPUTER VISION | "Computer Vision" is a specialized course designed to equip students with the knowledge and skills required to enable computers to interpret and understand the visual world. This course covers the fundamental concepts, techniques, and algorithms used in the field of computer vision, from basic image processing to advanced topics such as object recognition, 3D vision, and deep learning applications. | 3 | - | 3 |
- |
Students must pass ( 6 ) credit hours from any of the following courses
|
Course Number |
Course Name |
Weekly Hours |
Cr. Hrs. |
Prerequisite |
||
|---|---|---|---|---|---|---|
|
Theoretical |
Practical |
|||||
| 152696100 | TEXT MINING | This course is introduction to the field of text mining and will examine techniques used to analyze large amounts of data from traditional sources as well as the web and social media. Topics covered include text clustering, text classification, regression for text, information retrieval and search engines, text sequence modeling and deep learning, information extraction and opinion mining and sentiment analysis. This course will build on the foundation of Machine Learning and Natural Language Processing. | 3 | - | 3 |
- |
| 152696110 | DATA MINING | This course is an introduction to the techniques and applications of data mining and data analytics. Topics covered include preparing data for analysis, pattern mining, cluster analysis, outlier analysis, and mining different types of data sets such as data streams and graphical data. In addition, students will be exposed to new trends and techniques in data mining including mining web data and social network analysis. | 3 | - | 3 |
- |
| 152696120 | DESIGN OF ALGORITHM | This course is introduction to algorithm design and serves as a foundation for future courses in data science and artificial intelligence. Topics covered include common algorithms designs such as greedy optimization, dive and conquer, dynamic programming, network flows, reduction and randomized algorithms. Emphasis will be placed on using mathematical tools for designing and analyzing algorithms. | 3 | - | 3 |
- |
| 152696130 | BIG DATA ANALYTICS AND SCALABLE MACHINE LEARNING | "Big Data Analytics and Scalable Machine Learning" course designed to address the challenges and opportunities presented by large-scale data sets in the modern world. This course provides a comprehensive overview of big data analytics frameworks, scalable machine learning algorithms, and data engineering techniques necessary for extracting insights from massive, complex datasets. | 3 | - | 3 |
- |
| 152696140 | ETHICS AND RESPONSIBLE AI | The "Ethics and Responsible AI" course is designed to address the critical ethical considerations, societal impacts, and governance challenges associated with the development and deployment of artificial intelligence technologies. This interdisciplinary course aims to equip students with the knowledge and skills to design, develop, and deploy AI systems in a way that is ethical, responsible, and aligned with human values. | 3 | - | 3 |
- |
Thesis\Treatise Track: Total Credit Hours Required to Finish the Degree ( 36 Credit Hours ) as Follows
Specialization Requirements
Students must pass all of the following courses plus ( 6 ) credit hours for the Thesis
|
Course Number |
Course Name |
Weekly Hours |
Cr. Hrs. |
Prerequisite |
||
|---|---|---|---|---|---|---|
|
Theoretical |
Practical |
|||||
| 152696000 | RESEARCH METHODS | The course examines the role of business research and provides an overview of commonly used qualitative and quantitative research methods. Topics covered include scientific inquiry, the research process, proposal development, research design, hypothesis testing, primary and secondary data collection, statistical data analysis and presentation of research reports. | 3 | - | 3 |
- |
| 152696010 | INTRODUCTION TO DATA SCIENCE | This course is an introduction to the emerging field of data science, a field that combines business strategy, information technology and modeling methods. Topics covered include the evolution of data science, the benefits and opportunities of data science and its relation to machine learning and data infrastructure uses. Special emphasis will be placed on practical techniques for analyzing data including the use of statistical modeling as a data tool. | 3 | - | 3 |
- |
| 152696020 | DATA MANAGEMENT | This course is introduction to the concepts and theories of database management systems. Topics covered include data models, data integration and cleaning, database programming for extract, transform and load (ETL) operations, query optimization and database design. Special emphasis will be placed on query languages and search specifications, including Structured Query Language (SQL) for effective responses to user queries. | 3 | - | 3 |
- |
| 152696030 | DATA VISUALIZATION | This course is introduction to the theory and concepts of data visualization and the techniques used to create visual representation of large amounts of data. Topics covered include data representation, data and task abstraction, validation, tables and spatial data and maps and other channels. The emphasis will be on developing data visualization from a variety of sources and effectively communicating results. | 3 | - | 3 |
- |
| 152696040 | MACHINE LEARNING | This course provides an overview of machine learning and its growing role in the field of artificial intelligence. Students will examine different methods for machine learning and analyze how, why and when certain methods succeed. Topics covered include logistic regression, Bayesian Learning, tree-based models, neural networks, recommendation engines, and unsupervised and reinforcement learning. The emphasis will be on using statistical methods to build algorithms for machine learning. | 3 | - | 3 |
- |
| 152696050 | FOUNDATION OF AI | is course provides an overview of the field of artificial intelligence and its core techniques and applications. Topics covered include logic, constraint satisfaction, search, game playing, Markov decision processes and reasoning, planning and learning with certainty and uncertainty. Special emphasis will be placed on machine learning and its applications to real-world challenges. | 3 | - | 3 |
- |
| 152696060 | ADVANCED MACHINE LEARNING TECHNIQUES | This course is designed for students who have a foundational understanding of machine learning and wish to learn more about complex models and state-of-the-art techniques. The curriculum covers a broad spectrum of advanced machine learning methodologies, including ensemble methods, deep learning architectures, reinforcement learning, and transfer learning. Students will explore the theoretical underpinnings of these techniques, as well as their practical applications in solving real-world problems. | 3 | - | 3 |
- |
| 152696070 | DEEP LEARNING AND NEURAL NETWORK | This course" offers an intensive exploration into the realm of deep learning, focusing on the concepts, architectures, and applications of neural networks. This course aims to equip students with a deep understanding of how neural networks function, how they can be trained to recognize patterns and make predictions, and how they are applied to solve complex problems in areas such as image recognition, natural language processing, and more. | 3 | - | 3 |
- |
| 152696080 | NATURAL LANGUAGE PROCESSING | This course provides an overview of the ways that computers can process interpret written and spoken language and the role of natural language processing in artificial learning. Topics covered include processing text, classifying text, analyzing sentence structure and meaning, information extraction speech recognition and sentiment analysis. Emphasis will be placed on recent developments in computational linguistics and machine learning. | 3 | - | 3 |
- |
| 152696090 | COMPUTER VISION | "Computer Vision" is a specialized course designed to equip students with the knowledge and skills required to enable computers to interpret and understand the visual world. This course covers the fundamental concepts, techniques, and algorithms used in the field of computer vision, from basic image processing to advanced topics such as object recognition, 3D vision, and deep learning applications. | 3 | - | 3 |
- |
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