BS Artificial Intelligence

AI 202 Object Oriented Programming I
This course introduces the fundamental concepts of programming from an object-oriented perspective. Topics are drawn from classes and objects, abstraction, encapsulation, data types, calling methods and passing parameters, decisions, loops, strings, arrays and collections, documentation, testing and debugging, design issues, and inheritance. The course emphasizes modern software engineering and design. Three hours lecture, one hour laboratory.
Credits: 4: 3 hours lecture, 1 hour laboratory
Every Fall

AI 208 Algorithms and Data Structures
A study of the design and representation of information and storage structures and their associated implementation in a block-structured language; linear lists, strings, stacks, queues, multi- linked structures, representation of trees and graphs, iterative and recursive programming techniques; storage systems, structures and allocation; file organization and maintenance; and sorting and searching algorithms. Three hours lecture, one hour laboratory.
A pre requisite of AI 217 is required.
Credits: 3
Every Fall

AI 209 Discrete Structures
A study of the treatment of discrete mathematical structures and relevant algorithms used in the programming and computer science. Topics include the list, tree, set, relational and graph data models and their representation and use in searching, sorting and traversal algorithms; also, simulation, recursive algorithms and programming, analysis of running time of algorithms, and an introduction to finite-state machines and automata. Three hours lecture, one hour laboratory.
A co requisite of AI 208 is required.
Credits: 3
Every Fall

AI 210 Database Systems
The course is designed to impart the concepts and the practical aspects of database management systems and to provide an understanding of how data resources can be designed and managed to support information systems in organizations. Topics covered include: database system functions, Entity-Relationship (E-R) modeling, and relational database model, basic normalization techniques, data integrity, and SQL query language. Three credits; one-hour laboratory.
Credits: 3
Every Fall

AI 212 Data Mining and Business Intelligence
The course provides a comprehensive overview of the concepts behind data mining, text mining, and web mining. The course surveys various data mining applications, methodologies, techniques, and models. The course covers data mining case studies using large data sets from various domains. Three hours lecture, one hour laboratory. Three hours lecture, one hour laboratory.
A pre requisite of AI 208 and 218 is required.
Credits: 3
Every Fall

AI 213 Software Engineering
A study of software project management concepts, software cost estimation, quality management, process involvement, overview of analysis and design methods, user interface evaluation, and design. Also considered are dependable systems - software reliability, programming for reliability, reuse, safety-critical systems, verification and validation techniques; object-oriented development; using UML; and software maintenance. Three hours lecture, one hour laboratory
A pre requisite of AI 208 is required.
Credits: 3
Every Spring

AI 217 Object Oriented Programming I
This course covers  the most advanced features of the C++ programming language that are essential to the creation of complex structures and their applications in designing and  developing programs using software engineering concepts. (E.g.,  structures, objects and classes, function and operator overloading, collections, strings, recursion, file and string streams, pointers and dynamic data structures, inheritance and dynamic polymorphism, templates, exception handling, Standard Template Library (STL),  and advanced C++ topics ).   3 hours lecture, one-hour laboratory. A pre requisite of AI 102 is required.
Credits: 4:
Every Fall

AI 230 Algorithms and Data Structures Politics of the Middle East
A study of the design and representation of information and storage structures and their associated implementation in a block-structured language; linear lists, strings, stacks, queues, multi-linked structures, representation of trees and graphs, iterative and recursive programming techniques; storage systems, structures and allocation; file organization and maintenance; and sorting and searching algorithms. Three hours lecture, one-hour laboratory.
Credits: 3
Every Fall

AI 232 Discrete Structures
A study of the treatment of discrete mathematical structures and relevant algorithms used in the programming and computer science. Topics include the list, tree, set, relational and graph data models and their representation and use in searching, sorting and traversal algorithms; also, simulation, recursive algorithms and programming, analysis of running time of algorithms, and an introduction to finite-state machines and automata. Three hours lecture, one-hour laboratory. A co requisite of AI 130 is required. 
Credits: 3
Every Fall

AI 248 Database Systems
The course is designed to impart the concepts and the practical aspects of database management systems and to provide an understanding of how data resources can be designed and managed to support information systems in organizations. Topics covered include: database system functions, Entity-Relationship (E-R) modeling, and relational database model, basic normalization techniques, data integrity, and SQL query language.  Three credits; one-hour laboratory. Credits: 3
Every Fall

AI 262 Introduction to Artificial Intelligence
This course covers the basic principles of artificial intelligence. You will learn some basic AI techniques, the problems for which they are applicable, and their limitations. The course content is organized roughly around what are often considered to be three central pillars of AI: Search, Logic, and Learning. Topics covered include basic search, heuristic search, game search, constraint satisfaction, knowledge representation, logic and inference, probabilistic modeling, and machine learning algorithms. Three credits; one hour laboratory.
Credits: 3
Every Spring

AI 263 Data Mining and Business Intelligence
The study of advanced PROLOG programming, including advanced topics in knowledge representation and reasoning methods, which include semantic networks, frames non-monotonic reasoning and reasoning under uncertainty. A study is made of concepts and design techniques in application areas, such as natural-language processing, expert systems and machine learning. Introduction is made to genetic algorithms and neural networks. Three hours lecture, one hour laboratory.  Cross-listed with DA 163. A pre requisite of AI 130 and 162 is required.
Credits: 3
Every Fall

AI 264 Software Engineering
TA study of software project management concepts, software cost estimation, quality management, process involvement, overview of analysis and design methods, user interface evaluation, and design. Also considered are dependable systems - software reliability, programming for reliability, reuse, safety-critical systems, verification and validation techniques; object-oriented development; using UML; and software maintenance. Three hours lecture, one hour laboratory. A pre requisite of AI 130 is required.
Credits: 3
Every Spring

AI 230 Introduction to Algorithms 
This course motivates algorithmic thinking and focuses on the design of algorithms and the rigorous analysis of their efficiency. Topics include the basic definitions of algorithmic complexity (worst case, average case); basic tools such as dynamic programming, sorting, searching, and selection; advanced data structures and their applications; graph algorithms and searching techniques such as minimum spanning trees, depth first search, shortest paths, design of randomized algorithms and competitive analysis. Approximation algorithms are also briefly introduced. The pre requisite of AI 130 and AI 132 is required. Three credits; one-hour laboratory.
Credits: 3
Every Spring

AI 232 Theory Theory of Computation 
The course emphasizes theoretical models of computation and their analysis. The aim of the analysis is to identify and prove the capabilities and limitations of particular models of computation. The course investigates two fundamental questions about computing: 1) computability: can a problem be solved using a given abstract machine? And 2) complexity: how much time and space are required to solve the problem? The course explores these questions by developing abstract models of computation and reasoning about what they can do and cannot do efficiently. The abstract models include finite automata, regular languages, context-free grammars, and Turing machines. Additional topics covered include solvable and unsolvable problems, complexity classes P and NP, and NP-completeness. Three credits; one-hour laboratory. Prerequisites:  AI 230
Credits: 3
Every Fall

AI 233 Natural Language Processing
This course serves as an introduction to natural language processing (NLP), the goal of which is to enable computers to use human languages as input, output, or both. NLP is at the heart of many of today's most exciting technological achievements, including machine translation, automatic conversational assistants and Internet search. The course presents the variety of ways to represent human languages as computation systems, and how to exploit these representations to write programs that do useful things with text and speech data in the areas of translation, summarization, extracting information, question answering, and conversational agents. The course will connect some central ideas in machine learning (e.g. discrete classification) to linguistics (morphology, syntax, semantics).  Three credits; one-hour laboratory. A pre requisite of AI 162 is required.
Credits: 3
Every Spring

AI 234 Artificial Intelligence Language Understanding
The central focus of the course is to enable robust and effective human-computer interaction between humans and machines without supervision. To infer intent and deal with human language ambiguities in in text and speech, the course combines advanced concepts of Natural Language Processing, Neural Networks and Deep learning. Using core NLP technologies, the course takes an experimental approach to develop prototypes of chat and speech enabled intelligent agents that can effectively interact with the public without supervision. Three credits; one-hour laboratory. The pre requisite of AI 233 is required.
Credits: 3
Every Fall

AI 248 Introduction to Big Data Computing 
This course provides an in-depth coverage of various topics in big data from data generation, storage, management, to data analytics with focus on the state-of-the-art technologies, tools, architectures and systems that form today’s leading edge big data computing solutions in various industries. The course will focus on: the mathematical and statistical models that are used in learning from large scale data processing; the modern systems for cluster computing based on Map-Reduce pattern such as Hadoop MapReduce and Apache Spark; the implementation of big data solutions, including student projects on real cloud-based systems such as Amazon AWS, Google Cloud or Microsoft Azure. Three credits; one-hour laboratory. A pre requisite of AI 163 is required.
Credits: 3
Every Spring

AI 250 Machine Learning
Machine learning, a branch of Artificial Intelligence (AI), uses interdisciplinary techniques to create intelligent automated systems that can learn from examples, data, and experience. Such systems process large volumes of data at high speed to make predictions or decisions without human intervention. Machine learning as a field is now incredibly pervasive, with applications spanning from business intelligence to homeland security, from analyzing biochemical interactions to structural monitoring of aging bridges, from automated manufacturing to autonomous vehicles, etc. This class will familiarize students with a broad cross-section of models and algorithms for machine learning and their applications in various domains. Both supervised and unsupervised learning methods will be covered. Three credits; one-hour laboratory. A pre requisite of AI 162 is required.
Credits: 3
Every Spring

AI 255 Cloud Computing Concepts
The course presents a top-down view of cloud computing, from applications and administration to programming and infrastructure. Its main focus is on parallel programming techniques for cloud computing and large scale distributed systems which form the cloud infrastructure. The topics include: overview of cloud computing, cloud systems, parallel processing in the cloud, distributed storage systems, virtualization, security in the cloud, and multicore operating systems. Students will study state-of-the-art solutions for cloud computing developed by Google, Amazon, Microsoft, Yahoo, VMWare, etc. Students will also apply what they learn in one programming assignment and one project executed over Amazon Web Services.  Three credits; one-hour laboratory. pre requisite of AI 248 is required.
Credits: 3
Every Spring

AI 260 Deep Learning 
This course is an introduction to deep learning, a branch of machine learning concerned with the development and application of modern neural networks. Deep learning algorithms extract layered high-level representations of data in a way that maximizes performance on a given task. For example, asked to recognize faces, a deep neural network may learn to represent image pixels first with edges, followed by larger shapes, then parts of the face like eyes and ears, and, finally, individual face identities. Deep learning is behind many recent advances in artificial intelligence, including Siri’s speech recognition, Facebook’s tag suggestions, and self-driving cars.  A range of topics are covered which include basic neural networks, convolutional and recurrent network structures, deep unsupervised and reinforcement learning, and applications to various problem domains (e.g. speech recognition, computer vision, hand writing recognition, etc.).  Three credits; one-hour laboratory. A pre requisite of AI 250 is required.
Credits: 3
Every Spring

AI 265 Introduction of Modern Cryptography
Cryptography is the formal study of the notion of security in information systems. The course will offer a thorough introduction to modern cryptography focusing on models and proofs of security for various basic cryptographic primitives and protocols including key exchange protocols, commitment schemes, digital signature algorithms, oblivious transfer protocols and public-key encryption schemes. Applications to various problems in secure computer and information systems will be briefly discussed including secure multiparty computation, digital content distribution, e-voting systems, digital payment systems, and cryptocurrencies.  Three credits; one-hour laboratory.
Credits: 3
Every Spring

AI 299 Artificial Intelligence Capstone Project
The capstone project course is an integrative and experiential opportunity for students to apply the knowledge and skills that they have gained across the program curriculum. Students are encouraged to work in teams and can pursue either an applied or theory-based project.  Students who select applied projects participate in the identification of an artificial intelligence problem or challenge, develop a project proposal outlining an approach to the problem's solution, implement the proposed solution, and test or evaluate the results. Students who select a theory-based project conduct original research (e.g. develop a new algorithm or new heuristics) and evaluate its strengths and limitations. Students document their work in the form of written reports and oral presentations.  Three credits; one-hour laboratory. Co-requisite: AI 260.
Credits: 3
Every Spring