Thesis\Treatise Track: Total Credit Hours Required to Finish the Degree ( 48 Credit Hours ) as Follows
Specialization Requirements
Students must pass all of the following courses plus ( 18 ) credit hours for the Thesis
|
Course Number |
Course Name |
Weekly Hours |
Cr. Hrs. |
Prerequisite |
||
|---|---|---|---|---|---|---|
|
Theoretical |
Practical |
|||||
| 153938000 | ADVANCED MACHINE LEARNING | This course examines advanced machine learning techniques, focusing on c models like deep learning, probabilistic models, and reinforcement learning. Students will explore algorithms, optimization techniques, and applications across diverse fields. Emphasis is placed on understanding the theoretical foundation and practical implementations of these techniques. Topics like neural networks, ensemble methods, and unsupervised learning will be covered. Students will work on hands-on projects to develop sophisticated AI systems. | 3 | - | 3 |
- |
| 153938010 | DEEP LEARNING FOR NATURAL LANGUAGE PROCESSING (NLP) | This course explores deep learning applications in Natural Language Processing (NLP), focusing on neural networks such as transformers, BERT, and GPT models. Students will learn to tackle problems like machine translation, sentiment analysis, text generation, and information extraction. The course emphasizes hands-on practice, enabling students to design, train, and optimize NLP models. In addition, students will explore recent research trends in NLP and its role in AI-driven human language understanding. | 3 | - | 3 |
153938000 ADVANCED MACHINE LEARNING This course examines advanced machine learning techniques, focusing on c models like deep learning, probabilistic models, and reinforcement learning. Students will explore algorithms, optimization techniques, and applications across diverse fields. Emphasis is placed on understanding the theoretical foundation and practical implementations of these techniques. Topics like neural networks, ensemble methods, and unsupervised learning will be covered. Students will work on hands-on projects to develop sophisticated AI systems. |
| 153938020 | ADVANCED COMPUTER VISION | This course covers advanced topics in computer vision, emphasizing AI's role in visual data interpretation. Students will study techniques such as object detection, image segmentation, 3D vision, and video analytics, with a focus on deep learning-based approaches like convolutional neural networks (CNNs) and generative adversarial networks (GANs). The course combines theory and practice, preparing students to solve real-world problems in areas like autonomous systems, medical imaging, and augmented reality. | 3 | - | 3 |
153938000 ADVANCED MACHINE LEARNING This course examines advanced machine learning techniques, focusing on c models like deep learning, probabilistic models, and reinforcement learning. Students will explore algorithms, optimization techniques, and applications across diverse fields. Emphasis is placed on understanding the theoretical foundation and practical implementations of these techniques. Topics like neural networks, ensemble methods, and unsupervised learning will be covered. Students will work on hands-on projects to develop sophisticated AI systems. |
| 153938030 | REINFORCEMENT LEARNING AND DECISION MAKING | Students in this course will explore advanced reinforcement learning (RL) methods, with a focus on developing intelligent agents capable of autonomous decision-making. Topics include policy gradient methods, Q-learning, and deep RL, along with their applications in robotics, game AI, and resource management. Students will work on projects that involve designing RL algorithms to solve complex tasks, emphasizing both theoretical concepts and practical implementations. | 3 | - | 3 |
153938000 ADVANCED MACHINE LEARNING This course examines advanced machine learning techniques, focusing on c models like deep learning, probabilistic models, and reinforcement learning. Students will explore algorithms, optimization techniques, and applications across diverse fields. Emphasis is placed on understanding the theoretical foundation and practical implementations of these techniques. Topics like neural networks, ensemble methods, and unsupervised learning will be covered. Students will work on hands-on projects to develop sophisticated AI systems. |
| 153938040 | EXPLAINABLE AI (XAI) | This course investigates the growing need for transparent and interpretable AI systems, with a focus on making machine learning models more understandable to human users. Topics include model interpretability, post-hoc explanations, fairness, accountability, and transparency in AI systems. Students will explore tools and techniques to explain complex models like deep neural networks and study the ethical implications of AI in sensitive domains such as healthcare and finance. | 3 | - | 3 |
153938000 ADVANCED MACHINE LEARNING This course examines advanced machine learning techniques, focusing on c models like deep learning, probabilistic models, and reinforcement learning. Students will explore algorithms, optimization techniques, and applications across diverse fields. Emphasis is placed on understanding the theoretical foundation and practical implementations of these techniques. Topics like neural networks, ensemble methods, and unsupervised learning will be covered. Students will work on hands-on projects to develop sophisticated AI systems. |
| 153938090 | AI IN CYBERSECURITY | This course focuses on the application of AI techniques in cybersecurity, addressing challenges such as intrusion detection, threat hunting, and anomaly detection. Students will learn how machine learning and AI can be used to detect and respond to cybersecurity threats in real-time. The course covers topics such as network security, AI-driven encryption methods, and the role of AI in defending against evolving cyberattacks. Practical exercises will include building AI models to enhance security protocols. | 3 | - | 3 |
- |
| 153938140 | AI RESEARCH METHODOLOGIES | This course provides an in-depth overview of research methodologies specific to AI. Students will learn how to design experiments, analyze data, and develop algorithms that contribute to the field of AI. Topics include research ethics, publication strategies, and advanced algorithm development. The course is designed to equip students with the skills needed to conduct independent research, write high-impact papers, and contribute | 3 | - | 3 |
- |
| 153938150 | HUMAN-AI COLLABORATION AND INTERACTION | This course provides an in-depth overview of research methodologies specific to AI. Students will learn how to design experiments, analyze data, and develop algorithms that contribute to the field of AI. Topics include research ethics, publication strategies, and advanced algorithm development. The course is designed to equip students with the skills needed to conduct independent research, write high-impact papers, and contribute | 3 | - | 3 |
153938030 REINFORCEMENT LEARNING AND DECISION MAKING Students in this course will explore advanced reinforcement learning (RL) methods, with a focus on developing intelligent agents capable of autonomous decision-making. Topics include policy gradient methods, Q-learning, and deep RL, along with their applications in robotics, game AI, and resource management. Students will work on projects that involve designing RL algorithms to solve complex tasks, emphasizing both theoretical concepts and practical implementations. |
Students must pass ( 6 ) credit hours from any of the following courses
|
Course Number |
Course Name |
Weekly Hours |
Cr. Hrs. |
Prerequisite |
||
|---|---|---|---|---|---|---|
|
Theoretical |
Practical |
|||||
| 153938050 | AI FOR HEALTHCARE | Focusing on the intersection of AI and healthcare, this course explores how AI techniques can be applied to improve diagnostics, treatment planning, and patient outcomes. Topics include medical imaging analysis, clinical decision support systems, and personalized medicine. Students will learn to develop AI models for healthcare applications, while also addressing ethical concerns such as patient privacy, data security, and the regulatory environment in healthcare AI. | 3 | - | 3 |
153938000 ADVANCED MACHINE LEARNING This course examines advanced machine learning techniques, focusing on c models like deep learning, probabilistic models, and reinforcement learning. Students will explore algorithms, optimization techniques, and applications across diverse fields. Emphasis is placed on understanding the theoretical foundation and practical implementations of these techniques. Topics like neural networks, ensemble methods, and unsupervised learning will be covered. Students will work on hands-on projects to develop sophisticated AI systems. |
| 153938060 | COMPUTATIONAL NEUROSCIENCE AND COGNITIVE SYSTEMS | This course integrates concepts from AI and neuroscience, focusing on biologically inspired algorithms and their applications in cognitive systems. Students will explore how neural networks mimic brain function, study models of perception, learning, and decision-making, and investigate how computational approaches contribute to understanding human cognition. The course emphasizes interdisciplinary research, combining AI with insights from neuroscience and psychology. | 3 | - | 3 |
153938030 REINFORCEMENT LEARNING AND DECISION MAKING Students in this course will explore advanced reinforcement learning (RL) methods, with a focus on developing intelligent agents capable of autonomous decision-making. Topics include policy gradient methods, Q-learning, and deep RL, along with their applications in robotics, game AI, and resource management. Students will work on projects that involve designing RL algorithms to solve complex tasks, emphasizing both theoretical concepts and practical implementations. |
| 153938070 | AI FOR AUTONOMOUS SYSTEMS | course explores the use of AI in developing autonomous systems, including self-driving cars, drones, and robots. Students will study AI-driven technologies such as sensor fusion, real-time decision-making, and path planning. Topics include robotic perception, control systems, and AI for safe and reliable autonomy. The course offers hands-on projects where students will design and implement AI algorithms for autonomous navigation and control. | 3 | - | 3 |
153938030 REINFORCEMENT LEARNING AND DECISION MAKING Students in this course will explore advanced reinforcement learning (RL) methods, with a focus on developing intelligent agents capable of autonomous decision-making. Topics include policy gradient methods, Q-learning, and deep RL, along with their applications in robotics, game AI, and resource management. Students will work on projects that involve designing RL algorithms to solve complex tasks, emphasizing both theoretical concepts and practical implementations. |
| 153938100 | PROBABILISTIC GRAPHICAL MODELS | This course introduces students to probabilistic graphical models (PGMs) as a powerful framework for representing complex, uncertain systems. Students will study Bayesian networks, Markov random fields, and hidden Markov models, with applications in AI tasks such as decision-making, diagnosis, and prediction. The course covers both the theory and practical algorithms for inference and learning in PGMs. Students will apply these models to real-world problems in finance, healthcare, and natural language processing. | 3 | - | 3 |
153938000 ADVANCED MACHINE LEARNING This course examines advanced machine learning techniques, focusing on c models like deep learning, probabilistic models, and reinforcement learning. Students will explore algorithms, optimization techniques, and applications across diverse fields. Emphasis is placed on understanding the theoretical foundation and practical implementations of these techniques. Topics like neural networks, ensemble methods, and unsupervised learning will be covered. Students will work on hands-on projects to develop sophisticated AI systems. |
| 153938110 | AI FOR FINANCIAL TECHNOLOGY (FINTECH) | This course focuses on the role of AI in transforming financial services, covering applications such as automated trading, credit scoring, fraud detection, and personalized financial advice. Students will explore machine learning models and data analytics tools used to develop AI-driven financial solutions. The course emphasizes hands-on projects where students build AI applications for financial forecasting, risk management, and digital banking services. | 3 | - | 3 |
153938000 ADVANCED MACHINE LEARNING This course examines advanced machine learning techniques, focusing on c models like deep learning, probabilistic models, and reinforcement learning. Students will explore algorithms, optimization techniques, and applications across diverse fields. Emphasis is placed on understanding the theoretical foundation and practical implementations of these techniques. Topics like neural networks, ensemble methods, and unsupervised learning will be covered. Students will work on hands-on projects to develop sophisticated AI systems. |
| 153938120 | HUMAN-AI COLLABORATION AND INTERACTION | This course explores the design and development of AI systems that work in tandem with humans. Topics include human-computer interaction (HCI), AI-assisted decision-making, and interactive machine learning. Students will study how AI can enhance human capabilities in areas such as healthcare, education, and business, while also addressing challenges related to trust, usability, and ethical considerations in human-AI collaboration | 3 | - | 3 |
153938030 REINFORCEMENT LEARNING AND DECISION MAKING Students in this course will explore advanced reinforcement learning (RL) methods, with a focus on developing intelligent agents capable of autonomous decision-making. Topics include policy gradient methods, Q-learning, and deep RL, along with their applications in robotics, game AI, and resource management. Students will work on projects that involve designing RL algorithms to solve complex tasks, emphasizing both theoretical concepts and practical implementations. |
| 153938130 | LARGE-SCALE AI SYSTEMS AND DISTRIBUTED LEARNING | This course focuses on designing, deploying, and managing large-scale AI systems using distributed computing frameworks such as cloud computing and edge AI. Topics include distributed learning, federated learning, and optimization techniques for scaling AI models. Students will gain practical experience in building and deploying AI systems that can handle massive datasets and operate in real-time environments. The course prepares students for research and development of AI at scale. | 3 | - | 3 |
153938000 ADVANCED MACHINE LEARNING This course examines advanced machine learning techniques, focusing on c models like deep learning, probabilistic models, and reinforcement learning. Students will explore algorithms, optimization techniques, and applications across diverse fields. Emphasis is placed on understanding the theoretical foundation and practical implementations of these techniques. Topics like neural networks, ensemble methods, and unsupervised learning will be covered. Students will work on hands-on projects to develop sophisticated AI systems. |
Thesis\Treatise and Comprehensive Exam Track: Total Credit Hours Required to Finish the Degree ( 48 Credit Hours ) as Follows
Specialization Requirements
Students must pass all of the following courses plus ( 18 ) credit hours for the Thesis and Pass the Comprehensive Exam
|
Course Number |
Course Name |
Weekly Hours |
Cr. Hrs. |
Prerequisite |
||
|---|---|---|---|---|---|---|
|
Theoretical |
Practical |
|||||
| 153938000 | ADVANCED MACHINE LEARNING | This course examines advanced machine learning techniques, focusing on c models like deep learning, probabilistic models, and reinforcement learning. Students will explore algorithms, optimization techniques, and applications across diverse fields. Emphasis is placed on understanding the theoretical foundation and practical implementations of these techniques. Topics like neural networks, ensemble methods, and unsupervised learning will be covered. Students will work on hands-on projects to develop sophisticated AI systems. | 3 | - | 3 |
- |
| 153938010 | DEEP LEARNING FOR NATURAL LANGUAGE PROCESSING (NLP) | This course explores deep learning applications in Natural Language Processing (NLP), focusing on neural networks such as transformers, BERT, and GPT models. Students will learn to tackle problems like machine translation, sentiment analysis, text generation, and information extraction. The course emphasizes hands-on practice, enabling students to design, train, and optimize NLP models. In addition, students will explore recent research trends in NLP and its role in AI-driven human language understanding. | 3 | - | 3 |
153938000 ADVANCED MACHINE LEARNING This course examines advanced machine learning techniques, focusing on c models like deep learning, probabilistic models, and reinforcement learning. Students will explore algorithms, optimization techniques, and applications across diverse fields. Emphasis is placed on understanding the theoretical foundation and practical implementations of these techniques. Topics like neural networks, ensemble methods, and unsupervised learning will be covered. Students will work on hands-on projects to develop sophisticated AI systems. |
| 153938020 | ADVANCED COMPUTER VISION | This course covers advanced topics in computer vision, emphasizing AI's role in visual data interpretation. Students will study techniques such as object detection, image segmentation, 3D vision, and video analytics, with a focus on deep learning-based approaches like convolutional neural networks (CNNs) and generative adversarial networks (GANs). The course combines theory and practice, preparing students to solve real-world problems in areas like autonomous systems, medical imaging, and augmented reality. | 3 | - | 3 |
153938000 ADVANCED MACHINE LEARNING This course examines advanced machine learning techniques, focusing on c models like deep learning, probabilistic models, and reinforcement learning. Students will explore algorithms, optimization techniques, and applications across diverse fields. Emphasis is placed on understanding the theoretical foundation and practical implementations of these techniques. Topics like neural networks, ensemble methods, and unsupervised learning will be covered. Students will work on hands-on projects to develop sophisticated AI systems. |
| 153938030 | REINFORCEMENT LEARNING AND DECISION MAKING | Students in this course will explore advanced reinforcement learning (RL) methods, with a focus on developing intelligent agents capable of autonomous decision-making. Topics include policy gradient methods, Q-learning, and deep RL, along with their applications in robotics, game AI, and resource management. Students will work on projects that involve designing RL algorithms to solve complex tasks, emphasizing both theoretical concepts and practical implementations. | 3 | - | 3 |
153938000 ADVANCED MACHINE LEARNING This course examines advanced machine learning techniques, focusing on c models like deep learning, probabilistic models, and reinforcement learning. Students will explore algorithms, optimization techniques, and applications across diverse fields. Emphasis is placed on understanding the theoretical foundation and practical implementations of these techniques. Topics like neural networks, ensemble methods, and unsupervised learning will be covered. Students will work on hands-on projects to develop sophisticated AI systems. |
| 153938040 | EXPLAINABLE AI (XAI) | This course investigates the growing need for transparent and interpretable AI systems, with a focus on making machine learning models more understandable to human users. Topics include model interpretability, post-hoc explanations, fairness, accountability, and transparency in AI systems. Students will explore tools and techniques to explain complex models like deep neural networks and study the ethical implications of AI in sensitive domains such as healthcare and finance. | 3 | - | 3 |
153938000 ADVANCED MACHINE LEARNING This course examines advanced machine learning techniques, focusing on c models like deep learning, probabilistic models, and reinforcement learning. Students will explore algorithms, optimization techniques, and applications across diverse fields. Emphasis is placed on understanding the theoretical foundation and practical implementations of these techniques. Topics like neural networks, ensemble methods, and unsupervised learning will be covered. Students will work on hands-on projects to develop sophisticated AI systems. |
| 153938090 | AI IN CYBERSECURITY | This course focuses on the application of AI techniques in cybersecurity, addressing challenges such as intrusion detection, threat hunting, and anomaly detection. Students will learn how machine learning and AI can be used to detect and respond to cybersecurity threats in real-time. The course covers topics such as network security, AI-driven encryption methods, and the role of AI in defending against evolving cyberattacks. Practical exercises will include building AI models to enhance security protocols. | 3 | - | 3 |
- |
| 153938140 | AI RESEARCH METHODOLOGIES | This course provides an in-depth overview of research methodologies specific to AI. Students will learn how to design experiments, analyze data, and develop algorithms that contribute to the field of AI. Topics include research ethics, publication strategies, and advanced algorithm development. The course is designed to equip students with the skills needed to conduct independent research, write high-impact papers, and contribute | 3 | - | 3 |
- |
| 153938150 | HUMAN-AI COLLABORATION AND INTERACTION | This course provides an in-depth overview of research methodologies specific to AI. Students will learn how to design experiments, analyze data, and develop algorithms that contribute to the field of AI. Topics include research ethics, publication strategies, and advanced algorithm development. The course is designed to equip students with the skills needed to conduct independent research, write high-impact papers, and contribute | 3 | - | 3 |
153938030 REINFORCEMENT LEARNING AND DECISION MAKING Students in this course will explore advanced reinforcement learning (RL) methods, with a focus on developing intelligent agents capable of autonomous decision-making. Topics include policy gradient methods, Q-learning, and deep RL, along with their applications in robotics, game AI, and resource management. Students will work on projects that involve designing RL algorithms to solve complex tasks, emphasizing both theoretical concepts and practical implementations. |
Students must pass ( 6 ) credit hours from any of the following courses
|
Course Number |
Course Name |
Weekly Hours |
Cr. Hrs. |
Prerequisite |
||
|---|---|---|---|---|---|---|
|
Theoretical |
Practical |
|||||
| 153938050 | AI FOR HEALTHCARE | Focusing on the intersection of AI and healthcare, this course explores how AI techniques can be applied to improve diagnostics, treatment planning, and patient outcomes. Topics include medical imaging analysis, clinical decision support systems, and personalized medicine. Students will learn to develop AI models for healthcare applications, while also addressing ethical concerns such as patient privacy, data security, and the regulatory environment in healthcare AI. | 3 | - | 3 |
153938000 ADVANCED MACHINE LEARNING This course examines advanced machine learning techniques, focusing on c models like deep learning, probabilistic models, and reinforcement learning. Students will explore algorithms, optimization techniques, and applications across diverse fields. Emphasis is placed on understanding the theoretical foundation and practical implementations of these techniques. Topics like neural networks, ensemble methods, and unsupervised learning will be covered. Students will work on hands-on projects to develop sophisticated AI systems. |
| 153938060 | COMPUTATIONAL NEUROSCIENCE AND COGNITIVE SYSTEMS | This course integrates concepts from AI and neuroscience, focusing on biologically inspired algorithms and their applications in cognitive systems. Students will explore how neural networks mimic brain function, study models of perception, learning, and decision-making, and investigate how computational approaches contribute to understanding human cognition. The course emphasizes interdisciplinary research, combining AI with insights from neuroscience and psychology. | 3 | - | 3 |
153938030 REINFORCEMENT LEARNING AND DECISION MAKING Students in this course will explore advanced reinforcement learning (RL) methods, with a focus on developing intelligent agents capable of autonomous decision-making. Topics include policy gradient methods, Q-learning, and deep RL, along with their applications in robotics, game AI, and resource management. Students will work on projects that involve designing RL algorithms to solve complex tasks, emphasizing both theoretical concepts and practical implementations. |
| 153938070 | AI FOR AUTONOMOUS SYSTEMS | course explores the use of AI in developing autonomous systems, including self-driving cars, drones, and robots. Students will study AI-driven technologies such as sensor fusion, real-time decision-making, and path planning. Topics include robotic perception, control systems, and AI for safe and reliable autonomy. The course offers hands-on projects where students will design and implement AI algorithms for autonomous navigation and control. | 3 | - | 3 |
153938030 REINFORCEMENT LEARNING AND DECISION MAKING Students in this course will explore advanced reinforcement learning (RL) methods, with a focus on developing intelligent agents capable of autonomous decision-making. Topics include policy gradient methods, Q-learning, and deep RL, along with their applications in robotics, game AI, and resource management. Students will work on projects that involve designing RL algorithms to solve complex tasks, emphasizing both theoretical concepts and practical implementations. |
| 153938100 | PROBABILISTIC GRAPHICAL MODELS | This course introduces students to probabilistic graphical models (PGMs) as a powerful framework for representing complex, uncertain systems. Students will study Bayesian networks, Markov random fields, and hidden Markov models, with applications in AI tasks such as decision-making, diagnosis, and prediction. The course covers both the theory and practical algorithms for inference and learning in PGMs. Students will apply these models to real-world problems in finance, healthcare, and natural language processing. | 3 | - | 3 |
153938000 ADVANCED MACHINE LEARNING This course examines advanced machine learning techniques, focusing on c models like deep learning, probabilistic models, and reinforcement learning. Students will explore algorithms, optimization techniques, and applications across diverse fields. Emphasis is placed on understanding the theoretical foundation and practical implementations of these techniques. Topics like neural networks, ensemble methods, and unsupervised learning will be covered. Students will work on hands-on projects to develop sophisticated AI systems. |
| 153938110 | AI FOR FINANCIAL TECHNOLOGY (FINTECH) | This course focuses on the role of AI in transforming financial services, covering applications such as automated trading, credit scoring, fraud detection, and personalized financial advice. Students will explore machine learning models and data analytics tools used to develop AI-driven financial solutions. The course emphasizes hands-on projects where students build AI applications for financial forecasting, risk management, and digital banking services. | 3 | - | 3 |
153938000 ADVANCED MACHINE LEARNING This course examines advanced machine learning techniques, focusing on c models like deep learning, probabilistic models, and reinforcement learning. Students will explore algorithms, optimization techniques, and applications across diverse fields. Emphasis is placed on understanding the theoretical foundation and practical implementations of these techniques. Topics like neural networks, ensemble methods, and unsupervised learning will be covered. Students will work on hands-on projects to develop sophisticated AI systems. |
| 153938120 | HUMAN-AI COLLABORATION AND INTERACTION | This course explores the design and development of AI systems that work in tandem with humans. Topics include human-computer interaction (HCI), AI-assisted decision-making, and interactive machine learning. Students will study how AI can enhance human capabilities in areas such as healthcare, education, and business, while also addressing challenges related to trust, usability, and ethical considerations in human-AI collaboration | 3 | - | 3 |
153938030 REINFORCEMENT LEARNING AND DECISION MAKING Students in this course will explore advanced reinforcement learning (RL) methods, with a focus on developing intelligent agents capable of autonomous decision-making. Topics include policy gradient methods, Q-learning, and deep RL, along with their applications in robotics, game AI, and resource management. Students will work on projects that involve designing RL algorithms to solve complex tasks, emphasizing both theoretical concepts and practical implementations. |
| 153938130 | LARGE-SCALE AI SYSTEMS AND DISTRIBUTED LEARNING | This course focuses on designing, deploying, and managing large-scale AI systems using distributed computing frameworks such as cloud computing and edge AI. Topics include distributed learning, federated learning, and optimization techniques for scaling AI models. Students will gain practical experience in building and deploying AI systems that can handle massive datasets and operate in real-time environments. The course prepares students for research and development of AI at scale. | 3 | - | 3 |
153938000 ADVANCED MACHINE LEARNING This course examines advanced machine learning techniques, focusing on c models like deep learning, probabilistic models, and reinforcement learning. Students will explore algorithms, optimization techniques, and applications across diverse fields. Emphasis is placed on understanding the theoretical foundation and practical implementations of these techniques. Topics like neural networks, ensemble methods, and unsupervised learning will be covered. Students will work on hands-on projects to develop sophisticated AI systems. |
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