This course provides deep learning methods with applications to machine translation, image recognition, game playing, and more. Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks. The course covers the basic components of deep learning, what it means, how it works, and develop code necessary to build various algorithms. A
Senior Project II
Group project conducted by students who continue work on what is developed in AIS495 (Senior Project I). Each group should develop an integrated, complete and functional computing system or package for evaluation by a departmental technical review committee. Each team is also required to give a public presentation on their project.
Senior Project I
The group project is conducted by students who select a project topic according to their subject of interest and the availability of facilities and advisors. Each group carry out necessary research and development work and submit a detailed report. The report is submitted to a departmental committee for evaluation and discussion. In the second semester, each group should develop an integrated
Industrial/Research Training II
AI major Senior students are expected to get involved in industrial / research training for period of eight weeks minimum in Undergraduate Program of Computer Science (CS) or Undergraduate Program of Artificial Intelligence (AI) in Egypt or abroad. A detailed report followed by discussion is submitted to a departmental committee for evaluation.
Industrial/Research Training I
AI major Junior students are expected to get involved in industrial / research training for period of eight weeks minimum in Undergraduate Program of Computer Science (CS) or Undergraduate Program of Artificial Intelligence (AI) in Egypt or abroad. A detailed report followed by discussion is submitted to a departmental committee for evaluation.
Information and Cyber Security.
This course provides a principled introduction to the field of information security. History, characteristics and models of information and computer security are explored. It also provides an in-depth study of network attack techniques and methods to defend against them. Areas of study include communication security, infrastructure security, cryptography, and operational and organizational
Real Time Operating Systems
The This course introduces the basics of Real-Time Operating Systems (RTOSes) using VxWorks and Linux as examples. The course focuses on the primary principles of RTOSes including determinism, real-time scheduling, interrupt latency and fast context switching as well as time and space partitioning in hard real-time environments. The first part of the course focuses on acquiring an understanding of
The course provides an overview of topics in the field of Expert Systems. The course also provides the student with a working knowledge of designing an expert system and applying expert system technology in designing and analyzing knowledge engineering systems. The first part of the course covers historical background, knowledge acquisition and knowledge representation including propositional
High Performance Computing
To introduce students to the basics of High-Performance Computing, CUDA language, GPU Architecture and Parallel Programming Patterns including Reduction, Histogram.
Selected Topics in Undergraduate Program of Artificial Intelligence (AI)
This course is tailored to introduce students to the latest advances in the various fields in Undergraduate Program of Artificial Intelligence (AI), and/or to focus on a specific area of particular interest to the discipline. Examples include using AI in games, empowering disabled persons like deaf or other applications.
Introducing basic principles, concepts and modern technologies for the representation and management of knowledge and getting practical experience in the development of knowledge-based systems. Students will master the basic methods and tools for the development of knowledge-based systems
This course focuses on the Computational Intelligence domain and its heuristic algorithms such as fuzzy systems, evolutionary computation and neural networks. The course introduces elements of learning, adaptation, heuristic and meta-heuristic optimization techniques. The course employs these techniques in a wide range of application areas including decision support and classification.
The course provides different trends in Computer Vision that focus on advanced techniques of image processing mainly in the object recognition and 3D reconstruction fields.
Intelligent Decision Support Systems
This course introduces students to intelligent decision systems used in organizations. The course focuses on expert systems (ES) and decision support systems (DSS). Topics include decisions and decision making, decision support systems and expert systems, development approaches, artificial neural networks, and some cutting-edge intelligent technologies. The objective of this course is achieving a
Introducing basic principles, concepts and modern technologies for building NLP applications, mainly question answering systems and recommender system. The course introduces the students to the basic components of the QA systems. They include information retrieval, information extraction, natural language processing, and opinion mining. The course provides students with the basic understanding of