Artificial Intelligence
Student must complete 36 credit hours
Speciality Requirements Student must complete 27 credit hours
| Course Code | Course Name | Credit Hours | Prerequests |
|---|---|---|---|
| 491111 | Artificial Intelligence: A Modern Approach | 3 |
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| The Master's level course "Modern Approaches to Artificial Intelligence" offers an in-depth exploration of contemporary advancements, techniques, and applications within the field of AI. With a focus on cutting-edge methodologies and emerging trends, this course equips students with the knowledge and skills needed to navigate the complexities of modern AI systems. By the end of the course, students will have gained a comprehensive understanding of the latest advancements in AI, along with practical experience in applying modern AI techniques to solve complex problems. They will be well-equipped to contribute to cutting-edge research, innovation, and development within the field of artificial intelligence. | |||
| 491112 | Knowledge Representation and Reasoning | 3 |
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| This course is intended to provide students with enough information related to knowledge representation and analysis that contains subjects related to representing knowledge in several ways: Object Oriented, Structured Descriptions, Ontologies, and their related issues such as Semantic Web, OWL, Ontologies comparisons, Similarity Measures. The course will also deal with studying some real problems and applying different knowledge representations and analyses to them. | |||
| 491113 | Research Skills | 3 |
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| The principles of scientific research include presenting only some of the emerging foundations of scientific scientific philosophy and how to reach them Knowledge This course reviews the types of research in various sciences, in addition to the stages and steps of scientific implementation Starting from identifying and analyzing research problems, the concept of hypotheses of both probabilistic and other probabilistic types, and tools for collecting methods. Definition and psychometric properties, and explaining the concept of cooperation and its types, in addition to its special relationship to lack of research scientific. course output: Introduction concepts for scientific research The problem and hypotheses in scientific research Continue scientific research Choose scientific research Data collection tools in scientific research Research Methodology Developing a scientific research plan | |||
| 491114 | Machine and Deep Learning | 3 |
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| This course allows the student to identify, implement and develop various machine learning algorithms so that the computer can take decisions independently. This is done by analyzing the available data and learning from it in order to deal with any developments or required tasks Of which. Various automatic learning algorithms and techniques (Learning Supervised) and inferential learning are taught Unsupervised learning (such as artificial neural networks) and theoretical networks Bayesian Networks, Forest Random Forests, and Support Vectors (Machines Vector and Deep Learning), to name a few. ? Intended Learning Outcomes (ILO's) ? Distinguish between supervised and unsupervised learning methods. ? The ability to evaluate the appropriate technology to solve realistic problems in various fields. ? The ability to develop and train various machine learning techniques on real data. | |||
| 491115 | Computer Vision | 3 |
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| This course aims to teach the student what vision is and how to implement it using a computer. The student explores how to see when... human and links it to the components of computer vision and its basics, starting from the camera, image formation and processing, and even recognition. Its components, and this includes: taking photos and videos, preliminary processing, separating and identifying components, and representing parts of the image. Extracting its features and identifying them using machine learning algorithms. The student also studies how to track movement and vision Stereoscopic depth perception. ? Intended Learning Outcomes (ILO's) ? Learn the basics of digital images and vision. ? Explain the basic theories and techniques in computer vision. ? Understand and design image processing and enhancement techniques. ? Explain the concepts of image representation and recognition. ? Understand and use different machine learning techniques that can be used for image recognition. ? Understanding the principles of stereo vision and motion tracking. ? Design and implement computer vision algorithms for various applications. ? Helping companies and factories solve realistic problems | |||
| 491116 | Natural Language Processing | 3 |
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| The Natural Language Processing (NLP) course offers an extensive exploration of the theoretical foundations, computational techniques, and practical applications of processing human language using computers. NLP is a rapidly evolving field at the intersection of artificial intelligence, linguistics, and computer science, with wide-ranging applications in text analysis, information retrieval, machine translation, sentiment analysis, and more. By the end of the course, students will have acquired a comprehensive understanding of the theories, algorithms, and methodologies underlying natural language processing, as well as the practical skills to apply NLP techniques to solve real-world problems and contribute to advancements in the field. | |||
| 491121 | Data Mining and Analysis | 3 |
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| The Data Mining course offers a comprehensive exploration of the principles, techniques, and applications of mining valuable insights and patterns from large datasets. In an era where data is abundant but underutilized, data mining provides the essential tools and methodologies for transforming raw data into actionable knowledge, enabling informed decision-making and driving innovation across various industries and domains. Throughout the course, students will engage in hands-on exercises, projects, and assignments to reinforce theoretical concepts and develop practical data mining skills using popular tools and software libraries. By the end of the course, students will be equipped with the knowledge and tools necessary to leverage data mining techniques effectively in a wide range of analytical tasks and decision-making scenarios. | |||
| 491899 | Thesis | 6 |
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| The Thesis course is designed to guide students through the process of conducting independent research and producing a substantial scholarly work in their chosen field of study. This culminating experience allows students to demonstrate their mastery of the knowledge, skills, and methodologies acquired throughout their academic program. The Thesis course is typically undertaken in the final year of a graduate or undergraduate program, allowing students to integrate and apply their knowledge and skills in a focused research project. Students will work closely with a faculty advisor or thesis committee throughout the process, receiving guidance and mentorship to support their research endeavors. Prerequisites: Completion of relevant coursework in the student's field of study, including research methods or capstone courses. Students should have a strong foundation in academic writing, research design, and critical thinking skills. This course is open to graduate and undergraduate students across disciplines who are pursuing a thesis or equivalent research project as part of their degree requirements. The Thesis course provides an opportunity for students to make original contributions to their field of study and prepare for future academic or professional pursuits. | |||
Speciality Optional Requirements Student must complete 9 credit hours
| Course Code | Course Name | Credit Hours | Prerequests |
|---|---|---|---|
| 486512 | Advanced Data Structures and Algorithms | 3 |
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| Data structures designs for effective algorithms with concentration on advanced computing, Massive data structures, Advanced algorithm design methods, NP and NP completeness, Introduction to parallel algorithms and Heuristic algorithms. | |||
| 486577 | High Performance Computing | 3 |
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| Introduction to parallel computing, parallel processing and quantum computing. Parallel programming and parallel algorithm design, Parallel program debugging and performance computing, load balancing and parallel computing design. | |||
| 489111 | Principles and Design of IoT Systems | 3 |
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| It means the new generation of the Internet (network), which allows understanding between devices interconnected with each other (via the Internet Protocol). These devices include tools, sensors, sensors, various artificial intelligence tools, and others. ? Intended Learning Outcomes (ILO'S) ? Know how the public Internet works, as well as the Internet of Things. ? Understand the barriers and possibilities offered by wireless and mobile networks for the Internet of Things. ? Use basic metrics to determine the performance of packet-based networks in real time. ? Analysis of the wireless and wired sensor networks that underlie the Internet of Things. | |||
| 489121 | Smart Systems Applications | 3 |
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| The course deals with the study of practical applications of smart systems on the ground. The student studies several practical applications through analysis and Understanding each of the fields presented in the course, as well as studying smart applications in each field and how these applications work It contributed to improving the conditions and environment of each field in a way that reflects positively on it, through studying and analyzing each system. Examples of applications that will be presented in the course are: ? Automotive sector In the automotive sector, the integration of intelligent systems will be key to predictive driver assistance for safety Ways to reduce the number of deaths resulting from traffic accidents ? Internet of Things Intelligent systems also contribute significantly to the development of the future Internet of Things, as they provide intelligent functions to objects Everyday goods, such as industrial goods in the supply chain, or food products in the food supply chain. With technical assistance Active RFID, wireless sensors, real-time responsiveness, energy efficiency, In addition to networking functions, objects will become smart objects. These smart things can support the elderly and disabled. Close tracking and monitoring of food products can improve food supply and quality. Smart industrial goods can Storing information about their origin, destination, components and use. Waste disposal can become a process of recycling . a. Individual rotation is really effective ? Health care In the healthcare sector, intelligent systems technology leads to improved diagnostic tools, improved treatment and quality of life For patients by simultaneously reducing costs for public health care systems. | |||
| 489124 | Big Data Analytics | 3 |
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| The course covers a practical introduction to designing and building data processing systems on Google Cloud Platform. Students will learn how Design data processing systems, create end-to-end data pipelines, analyze data, and implement machine learning. The course covers Structured and unstructured data. Course outcomes: This course teaches the following skills: ? Design and build data processing systems on Google Cloud Platform ? Leverage unstructured data using Spark APIs ? Batch processing and data flow by implementing autoscaling data pipelines on Cloud Dataflow ? Enable instant insights from streaming data ? Extract, load, transform, clean, and validate data ? Design pipelines and architectures for data processing ? Create and maintain machine learning and statistical models ? Query data sets, visualize query results and create reports | |||
| 489128 | Intelligent Systems Management | 3 |
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| This course aims to provide students with the necessary skills to manage various smart projects, such as planning skills and the ability to Analysis, risk management, communication skill and communication with others, leadership skill in such systems, and time management. It also includes The course studies examples of successful and unsuccessful smart projects Course outcomes: Providing students with the necessary skills to manage various projects, such as planning skills, analytical ability, and risk management skills Communication and communication with others, leadership skills and time management. | |||
| 491117 | Robotics | 3 |
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| In this course, the student learns how to make, design, and build robots for various applications. The components of the robot are being studied for its desired mechanical movement. As it is done Electronic devices, control units and their algorithms, in addition to designing robots accordingly Study its applications and different types. ? Intended Learning Outcomes (ILO'S) ? Identify the main parts of robotics, which include processing units, control units, sensors, actuators, and Wireless communications. ? Understanding and designing different types of mobile robots. ? Exploring and studying various robotics applications and their problems, such as localization and navigation, exploring mazes, and creating Maps, real-time image processing, robot football, neural networks, genetic algorithms, systems the cars. ? The ability to build robots suitable for specific applications and within a special environment. ? Renewable energy and its applications | |||
| 491118 | Artificial Intelligenece Systems Applications 1 | 3 |
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| This course aims to introduce students to applications related to artificial intelligence in the field of spatial information systems Geographic information The main attention is paid to the uses of models based on multiple artificial neural networks. Classes, generalized regression, self-organizing networks and maps, statistical learning, vector support machines Key ideas of spatial classification, spatial prediction/mapping including automatic and non-linear algorithms Representation of multi-dimensional social and economic data, as well as its applications in the field of remote sensing and aerial image processing. All of these models are presented through reviewing case studies. ? Intended Learning Outcomes (ILO's) ? Learn about spatial information systems and their applications in practical life ? Identify the capabilities of artificial intelligence for spatial information systems applications ? Identify case studies for employing artificial intelligence models in the field of spatial information systems and their applications | |||
| 491119 | Artificial Intelligence Systems Applications 2 | 3 |
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| This course deals with applications of artificial intelligence in renewable energy systems, including important topics related to predicting radiation amounts. Solar and wind speed using neural cell systems and random forests, and the optimal use of these techniques Using algorithms that optimally determine the standards of these systems. In addition, this course aims to explain design algorithms Taking into account single design objectives and parameters or multi-objective methods. On the other hand, it offers Optimization of renewable energy systems is taking place for artificial intelligence. Finally, the course includes methods for installing distributed generation systems in electrical networks in an optimal way using algorithms The course presents some artificial intelligence algorithms through which renewable energy systems can be controlled ? Intended Learning Outcomes (ILO's) ? Learn about renewable energy systems and their applications in practical life ? Identify the capabilities of artificial intelligence for renewable energy systems applications ? Identify case studies for employing artificial intelligence models in the field of energy systems | |||
| 491120 | Special Topics | 3 |
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| This course covers emerging areas of artificial intelligence, and is also an introduction to the thesis. | |||