BS Artificial Intelligence

AI 102 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.

AI 117 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 130 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 132 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 148 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 162 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 163 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 164 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 300 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

BIO 1 Foundations of Biology
An introduction to the basic biological principles underlying the ways in which living organisms function. Topics such as the scientific method, cellular metabolism, cell division, heredity, and genetic engineering will be covered. Three hours lecture, three hours laboratory. This course fulfills the Scientific Inquiry and the Natural World thematic cluster requirement in the core curriculum.
Credits: 4
Every Fall, Spring and Summer

ECO 10 Introduction to Microeconomics
This course discusses the important economic theories and concepts that facilitate understanding economic events and issues. Its main focus is on the choices made by consumers, producers, and governments, and there interactions of these choices. Topics include demand and supply, consumption, and production, competitive and non-competitive product markets, markets for resources, and welfare. This course fulfills the Power, Institutions, and Structures thematic cluster requirement in the core curriculum.
Credits: 3
On Occasion

ENG 1 Writing I: Composition and Analysis
English 1 is an introductory writing course that uses interpretation and analysis of texts to promote clear thinking and effective prose. Students learn the conventions of academic writing. In addition, students learn how to adapt writing for various audiences and rhetorical situations. This course is required Writing I, an introduction to composition, teaches an understanding of writing in various disciplines through the interpretation and analysis of texts. Students will learn conventions of academic writing. Additionally, students will learn how to adapt in response to different rhetorical situations, genres, purposes, audiences, and other issues of context. Writing I is a course that provides the foundation for understanding how to make meaning from texts. This course is required of all students unless exempted by Advanced Placement credit or successful achievement on the SAT examination in writing. Students exempted by assessment or department proficiency examination must take an upper-level English course in substitution after completing ENG 2. Special sections are offered for students in the Program for Academic Success (P sections), for non-native speakers (F sections), and for students identified as needing more personalized attention (S sections). No Pass/Fail option.
Credits: 3
Every Fall, Spring and Summer

ENG 2  Writing II: Research and Argumentation
Writing II, a course in research and argumentation, focuses on scholarly research and the citation of information supporting sustained, rhetorically effective arguments. Building on the work of Writing I, this course addresses sensitivity to complex rhetorical and stylistic choices. Students will learn to use sources and resources effectively and ethically, including library holdings and databases, in service of scholarly arguments grounded in research. This course is required for all students unless exempted by Advanced Placement credit. Special sections are offered for students in the Program for Academic Success (P sections) and for non-native speakers (F sections). No Pass/Fail option. Prerequisite of ENG 1 is required.
Credits: 3
Every Fall, Spring and Summer

FY  First-Year Seminar and Post 101
Provide an emphasis upon the intellectual transition to college, first-year seminars focus on oral communication and critical reading skills taught in the context of theme-oriented academic courses specifically designed to meet the needs of first-year students. The content of these courses varies by discipline, but each course is limited to twenty students and linked in a learning community with a section of Post 101. First-Year Seminars involve intensive faculty mentoring and provide a source of support and insight to students who are encountering the new responsibilities connected to college life. First-Year Seminars can also be used to fulfill major requirements or can be used as electives, including, in many cases, liberal arts electives. Post 101 is best understood a one-credit course preparing first-year students for the challenges of college life. It emphasizes engagement with the campus community as a preparation for engagement with the world as an active, informed citizen. Weekly hour-long class meetings emphasize a holistic approach to learning and introduce students to the behavior, foundational skills, and intellectual aptitudes necessary for success.
Credits: 4 
Every Semester

MTH 5 Linear Mathematics for Business and Social Science
Mathematical models for business, linear programming, matrix algebra and applications are covered. Prerequisite of Math 4 or 4S is required. Not open to students who have taken MTH 8, except for Business Administration, Accountancy, or Dual Accountancy Students.
Credits: 3
Every Fall, Spring and Summer

MTH 7 Calculus and Analytic Geometry I
This course covers the derivative of algebraic and trigonometric functions with applications to rates,
maximization and graphing and integration, the Fundamental Theorem, and logarithmic and exponential functions. Cannot be taken for credit by any student who has completed or is currently taking MTH 1. Pre requisite of MTH 3 or MTH 3S with a grade of C- or better; or sufficiently high math SAT or ACT score as set by the department; or passing grade on the departmental placement test; or permission of department.
Credits: 4
Every Fall, Spring and Summer

MTH 8 Calculus and Analytic Geometry II
This course covers the applications of the definite integral, the calculus of trigonometric methods of integration, improper integrals and infinite series. Prerequisite of MTH 7 with a grade of C- or better or permission of Dept is required.
Credits: 4
Every Fall, Spring and Summer

MTH 9 Calculus and Analytic Geometry III
This course covers polar coordinates, vector and matrix algebra, parametric equations and space curves, multivariable calculus (gradients, relative extrema, Lagrange multipliers), surface areas and volumes by double and triple integrals, orthogonal coordinate systems and their Jacobian transformations, potential functions, compressibility, and the theorems of Gauss, Green, and Stokes. This course can fulfill an additional requirement the Scientific inquiry and the Natural World thematic cluster of the core curriculum alongside the laboratory science requirement. Prerequisite of MTH 8 with a grade of C- or better or permission of Dept. is required.
Credits: 4
Every Fall

MTH 22 Applied Linear Algebra
This course is an introduction to linear algebra that stresses applications and computational techniques. Topics covered include matrices, systems of linear equations, determinants, vector spaces and linear transformations, eigenvalues and eigenvectors. This course can fulfill an additional requirement the Scientific inquiry and the Natural World thematic cluster of the core curriculum alongside the laboratory science requirement. Prerequisite of MTH 8 is required.
Credits: 3
Every Spring

MATH 51 Probability
This course covers probability theory with applications to discrete and continuous random variables.  Prerequisites of MTH 9 and 20 or department permission are required.
Credits: 3
Every Spring

PHY 3 University Physics I
Physics 3 is the first half of an introductory, calculus-based, physics course for science and mathematics majors, covering the laws and principles of mechanics, thermodynamics, and waves. Four hours lecture, two hours laboratory. This course fulfills the Scientific Inquiry and the Natural World thematic cluster requirement in the core curriculum. Prerequisite or co-requisite of MTH 7 is required.
Credits: 4
Every Fall, Spring and Summer

PHY 4 University Physics I
Physics 4 is the second half of an introductory, calculus-based physics course for science and mathematics majors. It is concerned with the laws and principles of electricity, magnetism, and optics, and includes and introduction to modern physics. Four hours lecture, two hours laboratory. This course fulfills the Scientific Inquiry and the Natural World thematic cluster requirement in the core curriculum. Prerequisites of PHY 3 and MTH 7 and corequisite of MTH 8 are required.

Credits: 4
Every Fall, Spring and Summer

Post 101 and FY  First-Year Seminar
Provide an emphasis upon the intellectual transition to college, first-year seminars focus on oral communication and critical reading skills taught in the context of theme-oriented academic courses specifically designed to meet the needs of first-year students. The content of these courses varies by discipline, but each course is limited to twenty students and linked in a learning community with a section of Post 101. First-Year Seminars involve intensive faculty mentoring and provide a source of support and insight to students who are encountering the new responsibilities connected to college life. First-Year Seminars can also be used to fulfill major requirements or can be used as electives, including, in many cases, liberal arts electives. Post 101 is best understood a one-credit course preparing first-year students for the challenges of college life. It emphasizes engagement with the campus community as a preparation for engagement with the world as an active, informed citizen. Weekly hour-long class meetings emphasize a holistic approach to learning and introduce students to the behavior, foundational skills, and intellectual aptitudes necessary for success.
Credits: 4
Every Semester