Bachelor of Science in Artificial Intelligence

Sample Four Year Study Plan

 Freshman Year, Semester 1 (Fall)
Code Course Title Cr
CSCI 104 Introduction to Computing  3
ENGL 101 Composition 3
MATH 113 Calculus I 4
PHYS 110 University Physics I 3
PHYS 111 University Physics I Lab 1
UNIV 100 University First-Year Transition 1
Total 15
 Sophomore Year, Semester 1 (Fall)
Code Course Title Cr
CSCI 211 Object-Oriented Programming 3
MATH 213 Calculus III 3
MATH 225 Discrete Mathematics 3
CSCI 232 Computer Organization 3
Gen Ed Social and Behavioral Sciences 3
Total 15
Sophomore Year, Semester 2 (Spring)
Code Course Title Cr
CSCI 215 Data Structures and Algorithms 3
ECEN 331 Digital System Design 3
ECEN 332 Digital Systems Design Lab  1
STAT 346 Probability for Engineers 3
PHIL 100
ENGL 200
Critical Thinking and Reasoning 
Advanced Composition
MATH 203 Linear Algebra 3
Total 16
 Junior Year, Semester 1 (Fall)
Code Course Title Cr
CSAI 350 Introduction to Artificial Intelligence 3
Gen Ed Natural Sciences 3
CSCI 312 Operating System Fundamentals 3
CSCI 326 Database Systems 3
UNIV 200 Innovation, Entrepreneurship and Sustainability 3
Total 15
Junior Year, Semester 2 (Spring)
Code Course Title Cr
CSCI 315 Design and Analysis of Algorithms 3
CSAI 351 Data Science 3
CSCI 462 Data Communications and Computer Networks  3
CSCI 415 Introduction to Parallel Programming  3
UAES 200 Survey of United Arab Emirates Studies 3
Total 15
 Junior Year, Summer Semester
Code Course Title Cr
ENGR 390 Internship 3
Total   3
 Senior Year, Semester 1 (Fall)
Code Course Title Cr
CSAI 450 Machine Learning 3
CSAI 451 Machine Learning Lab 1
CENG 411 Software Engineering 3
CSCI 492 Senior Design Project I 2
CSAI 490 Professional Software Practice 2
  Technical Elective 1 3
Total   14
Senior Year, Semester 2 (Spring)
Code Course Title Cr
CSCI 493 Senior Design Project II 4
CSAI 452 Natural Language Processing 3
CSAI 453 Data Visualization 3
  Technical Elective II 3
  Technical Elective III 3
Total   16
Last updated: Apr 5, 2021 @ 11:41 am

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CSCI 104 - Introduction to Computing 

This course serves as an introduction to the field of computer science and the computer’s various layers. The course provides exposure to the following layers: information, hardware, programming, operating systems, applications, and communications. Additional topics include ethics, security, privacy, the impact of computing, and widely used software applications

ENGL 101 - Composition

Pre-requisite(s): ENGL 099 or passing English Placement Test

English 101 provides students with intensive practice in drafting, revising, and editing expository essays for an academic audience. Using logical, rhetorical, and linguistic structures in their writing, students also develop their ability to think creatively, critically, and independently. Throughout the course, students engage in reading texts, evaluating sources, using their reading to form their own opinions, preparing research papers, and employing the MLA documentation style to avoid plagiarism.

MATH 113 - Calculus I

Pre-requisite(s): MATH 095/105, or appropriate score on Math Placement test

The concept of derivative (instantaneous rate of change) is an essential factor in solving real-world problems. One of the objectives of this course is to understand the conceptual foundation of derivative, and learn different techniques of computing the derivative, as well as learning how to apply it to solve real-world problems. Another objective is to understand the concept of integration and learn basic integration technique.

PHYS 110 - (SCPH 110) University Physics I

Co-requisites: PHYS 111

This is a calculus-based physics course covering the fundamental principles of mechanics. It concentrates on the conservation of energy, the particle motion, the collisions, the rotation of solid bodies, simple machines and on the fluid mechanics. The focus lies on the resolution of one and twodimensional mechanical problems.

PHYS 111 - (SCPH 111) University Physics I Lab

Co-requisites: PHYS 110

This course is intended to be taken with Physics 110. It primarily includes experiments on classical mechanics. Particular emphasis is placed on laboratory technique, data collection and analysis and on reporting.

UNIV 100 - University First-Year Transition

Students in this course transition to university life by focusing on academic adjustment, by developing decision-making skills, and by learning about services and opportunities for involvement. Although all classes have a core body of knowledge, each class specializes in a particular aspect of university life.

ARAB 101 Beginner Level Arabic and Culture for non-Native Learners I

Pre-requisite: None

Beginner Level Arabic Language and Culture 1 is the first in a four-course beginner and intermediate Arabic language sequence specifically tailored to the needs of non-native Arabic language students in the English and Mass Communication Programs (though any non-native learner of Arabic may enroll). This course introduces the student to the Arabic alphabet and the basics of reading and writing in Modern Standard Arabic (MSA). Instruction in the language is enriched by reference to cultural themes and visits to sites of cultural importance.

ARAB 110 - Arabic Language and Culture for Native Arabic Speakers I

Pre-requisite(s): ENGL 101

Arabic literature has developed many traditions though originating from a common source. The course is an introduction to representative texts from contemporary Arab writers, and their connections with the traditions of the past. The method is comparative, with a study of literary, political social and religious aspects, as well as the application of a theoretical framework of analysis.

CSCI 112 - Introduction to Computer Programming

Co-requisite(s): CSCI 113

This course introduces students to computers and programming languages and more specifically the C++ language. Besides, students learn to use computer programming as a problem-solving tool. The topics covered include basic operations, data types, input/output, selection statements, control structures, arrays, functions, and strings.

CSCI 113 - Introduction to Computer Programming Lab

Co-requisite(s): CSCI 112

This course introduces the use of computer programming as a problem-solving tool in laboratory environment. Topics in procedural programming include, simple data types, input/output, selection statements, control loops, testing, debugging, and programming environments.

MATH 114 - Calculus II

Pre-requisite(s): MATH 113

This course covers techniques and applications of integration, transcendental functions, infinite sequences and series and parametric equations.

PHYS 220 - (SCPH 220) University Physics II

Pre-requisite: PHYS 110
Co-requisite: PHYS 221

This second calculus-based physics course includes a detailed study of the fundamental principles of classical electricity and magnetism, as well as an introduction to electromagnetic waves. The course’s focus targets the resolution of dc- and alternating circuits.

PHYS 221 - (SCPH 221) University Physics II Lab

Co-requisites: PHYS 220

This course is intended to accompany Physics 220. It includes experiments on electricity, magnetism and RLC circuits. Particular emphasis is placed on three aspects of experimentation: laboratory technique, data analysis (including the treatment of statistical and systematic errors) and written communication of experimental procedures and results.

MEST 100 - Introduction to Islam in World Culture 

The course provides an introduction to the basic sources and historical contexts for the origins of Islam; some of the basic spiritual principles expressed in those sources; the contexts and practices that exemplify the spiritual principles; contributions Islam has made to civilization and to the political, social and cultural identity of the UAE. It will illustrate the concept of Islamic studies through a global, interdisciplinary and comparative approach and examine contemporary global and local issues that impact and are impacted by Islamic culture.

CSCI 211 - Object-Oriented Programming

Pre-requisite(s): CSCI 112

This course is an introduction to object-oriented programming principles and techniques using Java. Topics include Java elementary programming, and Java object-oriented features such us methods, objects, classes, access modifiers, constructors, immutable objects & classes, abstraction, encapsulation, inheritance, polymorphism, dynamic binding, object castings, abstract and interface classes, and exception handling.

MATH 213 - Calculus III

Pre-requisite(s): MATH 114

This course covers partial differentiation, multiple integrals, line and surface integrals, and threedimensional analytic geometry.

MATH 225 - Discrete Mathematics

Pre-requisite(s): MATH 113

This course covers the basic discrete mathematical structure, methods of reasoning, and counting techniques: sets, equivalence relations, propositional logic, predicate logic, induction, recursion, pigeon-hole principle, permutation and combinations.

CSCI 232 - Computer Organization

Pre-requisite(s): CSCI 112

This course provides a programmer’s view of the execution of programs in computer systems. Topics covered include instruction sets, machine-level code, assembly language, performance evaluation and optimization, memory organization and management, address translation, and virtual memory.

CSCI 215 - Data Structures and Algorithms

Pre-requisite(s): CSCI 211 and MATH 225

This course introduces data structures and various fundamental computer science algorithms. The course covers abstract data-type concepts, stacks, queues, lists, and trees. Several sorting and searching algorithms are covered. Additional topics include an introduction to graphs and their implementation and running time and time complexity measurement.

ECEN 331 - Digital System Design (3:3:0)

Pre-requisite(s): PHYS 220
Co-requisite(s): ECEN 332

This course covers principles of digital logic and digital system design. Topics include number systems; Boolean algebra; analysis, design, and minimization of combinational logic circuits; analysis and design of synchronous and asynchronous finite state machines; and an introduction to VHDL and behavioral modeling of combinational and sequential circuits.

ECEN 332 - Digital Systems Design Lab (1:0:3)

Pre-requisite(s): PHYS 220
Co-requisite(s): ECEN 331

Laboratory course to accompany ECEN 331. In this course, the student will acquire hands-on experience with basic logic components, combinational and sequential logic circuits and the use of VHDL.

STAT 346 - Probability for Engineers

Pre-requisite(s): MATH 114

The course introduces principles of statistics and probability for undergraduate students in Engineering. The course covers the basic concepts of probability, discrete and continuous random variables, probability distributions, expected values, joint probability distributions, and independence. The course also covers statistical methods and topics including data summary and description techniques, sampling distributions, hypothesis testing, and regression analysis.

PHIL 100 - Critical Thinking and Reasoning 

This introduction to basic principles of reasoning and critical thinking enhances the learner’s abilities to evaluate various forms of reasoning in everyday life and in academic disciplines. The course explores such topics as inductive and deductive reasoning, the nature and function of definitions, fallacy types, statistic use and misuse, and the rudiments of logic.

ENGL 200 (ENGL 302) Advanced Composition

Pre-requisite(s): Completion of a minimum of 36 credit hours and credit for ENGL 101 (Completion of 45 credits)

This course builds on the general college-level writing skills and strategies students have acquired in earlier courses, and prepares them to do advanced level analysis and writing specifically within their major field and their possible future workplaces.

MATH 203 - Linear Algebra

Pre-requisite(s): MATH 113

This course covers systems of linear equations, linear independence, linear transformations, inverse of a matrix, determinants, vector spaces, eigenvalues, eigenvectors, and diagonalization.

CSAI 350 - Introduction to Artificial Intelligence (3 credits)
Pre-requisite(s): MATH 225 and STAT 346

This course provides an introduction to the different sub-areas of Artificial Intelligence (AI).  In addition, students learn basic concepts, methods and algorithms of AI and how they can be used to solve practical AI problems. The topics include classical and adversarial search & heuristic, knowledge representation, probabilistic reasoning, convex optimization methods, Bayesian methods, reinforcement learning, and supervised and unsupervised learning techniques. Particular focus will be placed on real-world applications of the material.

CSCI 312 - Operating System Fundamentals

Pre-requisite(s): CSCI 215

This course covers the principles, components, and design of modern operating systems, focusing on the UNIX platform. Topics include system structure, process concept, multithreaded programming, process scheduling, synchronization, atomic transaction, deadlocks, memory management, and file system.

CSCI 326 - Database Systems

Pre-requisite(s): CSCI 211

This course is an introductory course on database management systems. The goal of the course is to present a comprehensive introduction to the use of data management systems. Some of the topics covered are the following: The Entity-Relationship Model, the Relational Data Model, the SQL language, the database design, and the database integrity and security.

UNIV 200 - Innovation, Entrepreneurship and Sustainability (3 Credits)

This course aims at equipping the next generation of leaders in the UAE with an innovative and entrepreneurial mindset and its related core skills. The course combines three main points: design thinking, entrepreneurship, and growth and leadership.

CSCI 315 - Design and Analysis of Algorithms

Pre-requisite(s): CSCI 215

This course introduces the design and analysis principles for various algorithms. The topics covered include searching algorithms, dynamic programming, greedy algorithms, Huffman coding, graph traversing algorithms, shortest path algorithms, linear programming, and NP-completeness.

CSAI 351 - Data Science (3 credits)

Pre-requisite(s): CSAI 350 and MATH 203

This course provides an introduction to data science and highlights its importance in real world context. Topics include data science concepts, project lifecycle, tools & programming environment, fundamentals of Python programming, numerical processing, data visualization, exploratory data analysis, data preprocessing, parameter optimization, model performance evaluation, and applications of machine learning algorithms in Python (i.e., Naïve Bayes, k-Nearest Neighbors, Linear/Multiple/Logistic Regressions, Decision Trees, and Clustering Applications), natural language processing, and real-world data science case studies.

CSCI 462 - Data Communications and Computer Networks 

Pre-requisite(s): CSCI 112

This course introduces computer networks. Topics include layering approach, functions of different layers, Internet applications (HTTP, DNS), reliable and unreliable transport (TCP and UDP), routing and IP addressing, data link layer services and protocols, and Ethernet.

CSCI 415 - Introduction to Parallel Programming 

Pre-requisite(s): CSCI 215

This course is an introduction to parallel programming principles and techniques. Topics include parallel computing memory architecture, memory organization, parallel programming models, parallel program design, performance evaluation, thread-based parallelism, process-based parallelism, message passing, asynchronous programming, and heterogeneous programming.

ENGR 390 - Internship

Pre-requisite(s): Completion of 90 credits and a cumulative GPA of 2.0 or higher

Supervised field experience of professional-level duties for 180 to 240 hours at an approved internship site under the guidance of a designated site supervisor in coordination with a faculty supervisor.

CSAI 450 - Machine Learning (3 credits)

Pre-requisite(s): CSAI 350 and CSAI 351
Co-requisite(s): CSAI 451

This course introduces fundamental concepts of machine learning, and provides students with knowledge and understanding of the methods, mathematics, and algorithms used in machine learning. Topics include statistical learning concepts, linear & quadratic discriminant analysis, resampling methods, model selection and regularization, regression & smoothing splines, generalized additive models, regression trees, bagging and boosting, support vector machines, principal components analysis, k-means clustering, hierarchical clustering, and neural networks.

CSAI 451 - Machine Learning Lab (3 credits)

Co-requisite(s): CSAI 450

This course, which is conducted within a laboratory environment, aims to familiarize students with several techniques used in machine learning. The topics covered include Linear Regression, Classification, Resampling, Linear Model Selection, Tree-Based Methods, Support Vector Machines, and Neural Networks.

CENG 411 - Software Engineering

Prerequisite(s): CSCI 215

This course examines in detail the software development process. Topics include concepts such as software processes, software specification, software design implementation, software testing, software evolution, and software reuse.

CSCI 492 - Senior Design Project I

Pre-requisite(s): Senior standing

The course requires seniors to work in small teams to solve significant problems. Over the duration of CSCI 492 and CSCI 493, students design, implement, and evaluate a solution to the problem in conjunction with a faculty advisor. The course reinforces programming principles and serves as a capstone for computing knowledge obtained in the BSCS curriculum. The recognition of the ethical and legal principles are also aspects of the course.

CSAI 490 - Professional Software Practice (2 credits)
Pre-requisite(s): Senior standing, Co-requisite(s): CSCI 492

The course develops student understanding about historical, social, economic, ethical, and professional issues related to the discipline of Computing. It identifies key sources for information and opinion about professionalism and ethics. Students analyze, evaluate, and assess ethical and professional computing case studies

CSCI 493 - Senior Design Project II

Pre-requisite(s): CSCI 492

Implementation of the project for which preliminary work was done in CSCI 492. Project includes designing and constructing software and/or hardware, conducting experiments or studies, and testing and validating a complete system. At the end of the term, each team presents to a committee information related to its project in both written and oral formats.

CSAI 452 - Natural Language Processing (3 credits)

Pre-requisite(s): CSAI 450

This course introduces the fundamental concepts and techniques of natural language processing (NLP). Topics include text corpora and conditional frequency distributions, lexical resources and WordNet, raw text processing and regular expressions, text normalization and lemmatization, structured natural language processing (NLP) programs, part-of-speech tagging, automatic tagging, n-gram, & transformation-based tagging, document and sequence classification, maximum entropy classifiers and modeling linguistic patterns, information extraction, linguistic structure, named entity recognition, & relation extraction, grammatical structure & context free grammar, context free grammar parsers & dependency grammar, and feature based grammars.

CSAI 453 - Data Visualization (3 credits)

Prerequisite(s): CSAI 350

Data visualization is an essential skill required in today’s data-driven world. This course presents principles and techniques to design and create data visualization based on gathered data and the goals of the task at hand. Topics include the value of visualization, data, tasks, validation, marks and channels, design guidelines, tables, networks and trees, spatial, temporal and textual data, interaction and navigation, and data reduction.