Bachelor of Science in Artificial Intelligence

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Program Requirements

The BS in Artificial Intelligence requires the completion of 127 credits in the following areas:

Degree Requirements Credits
University General Education Requirements 33
School of Engineering Requirements 24
Artificial Intelligence Program Requirements 70
(61 compulsory & 9 technical electives)
Total 127

University General Education Requirements (33 credit hours)
University General Education Requirements are (33) credit hours, as follows:

a. Orientation Courses (14) credit hours required

Code Course Title Credit
ARAB 101
OR
ARAB 110		 Beginner Level Arabic and Culture for non-Native Learners I or
Arabic Language and Culture for Native Arabic Speakers I 		3
ENGL 101 Composition 3
CSCI 112 Introduction to Computer Programming 3
CSCI 113 Introduction to Computer Programming Lab 1
UNIV 100 University First-Year Transition 1
UNIV 200 Innovation, Entrepreneurship and Sustainability 3

b. Knowledge Domains: Divided into the following three categories: Humanities and Fine Arts, Social and Behavioral Sciences, and the Natural Sciences.

1. Humanities and Fine Arts (6 credits minimum)

PHIL 100
OR
ENGL 200		 Critical Thinking and Reasoning 
or
Advanced Composition 		3
MEST 100 Introduction to Islam in World Culture  3

2. Social and Behavioral Sciences (6 credits required)

UAES 200 Survey of United Arab Emirates Studies 3*
PSYC 100 Introduction to Psychology 3
ECON 103 Principles of Microeconomics  3
POLI 100 Contemporary Global Issues 3
POLI 101 Politics of Scarcity  3
GEOG 100 World Regional Geography  3
COMM 101 Interpersonal Communication and Group Interaction 3

* UAES 200 is mandatory

3. Natural Sciences (7 credits required)

MATH 113 Calculus I 4*
BIOL 100 Humankind in a Biological World 3
CHEM 100/101 Chemistry in Everyday Life 3
CHEM 211/212 General Chemistry I  3
ENVS 102 Sustainability and Human-Environment Relations 3

* MATH 113 is mandatory

School of Engineering Requirements (24 credit hours)

PHYS 110 University Physics I 3
PHYS 111 University Physics I Lab 1
MATH 114 Calculus II 4
MATH 213 Calculus III 3
PHYS 220 University Physics II 3
PHYS 221 University Physics II Lab 1
MATH 203 Linear Algebra 3
STAT 346 Probability for Engineers 3
ENGR 390 Internship 3

Artificial Intelligence and Data Science Program Requirements (70 credit hours)

a. Core Courses (61 credit hours)

MATH 225 Discrete Mathematics 3
CSCI 104 Introduction to Computing  3
CSCI 211 Object-Oriented Programming 3
CSCI 215 Data Structures and Algorithms 3
CSCI 232 Computer Organization 3
CSCI 315 Design and Analysis of Algorithms 3
CSCI 326 Database Systems 3
CSCI 312 Operating System Fundamentals 3
CSAI 350 Introduction to Artificial Intelligence 3
CSAI 351 Data Science 3
ECEN 331 Digital System Design 3
ECEN 332 Digital Systems Design Lab  1
CENG 411 Software Engineering 3
CSCI 415 Introduction to Parallel Programming  3
CSAI 450 Machine Learning 3
CSAI 451 Machine Learning Lab 1
CSCI 462 Data Communications and Computer Networks  3
CSAI 452 Natural Language Processing 3
CSAI 453 Data Visualization 3
CSAI 490 Professional Software Practice 2
CSCI 492 Senior Design Project I 2
CSCI 493 Senior Design Project II 4

b. Technical Electives (9 credit hours minimum)

CSCI 450 Information Security and Privacy 3
CSAI 480 Big Data 3
CSAI 481 Computer Vision 3
CSAI 482 Data Mining 3
CSAI 483 Information Retrieval 3
CSAI 484 Internet of Things System 3
CSAI 485 Introduction to Deep Learning 3
CSCI 416 Human Computer Interaction 3
CENG 431 Embedded Systems Design 3
CENG 432 Embedded Systems Design Lab 1
CSAI 486 Special Topics in Artificial Intelligence 3
CSAI 487 Introduction to Robotics 3
ENGR 399 Undergraduate Research Project 3
Total 127 Credits 

 

Last updated: Apr 5, 2021 @ 10:47 am

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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.

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.

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.

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.

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.

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.

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.

PSYC 100 - Introduction to Psychology

This course provides an overview of major areas in the field of psychology. The following topics will be covered in this course: history of psychology; research methods used in psychology; organization of human brain and biological basis of behavior; sensation; perception; basic principles of learning; cognition; language; intelligence; emotion; motivation; developmental psychology; personality theories and assessment, stress and its effect on health; abnormal behavior and therapies; and, social psychology.

ECON 103 - (GEEC 103) Principles of Microeconomics 

This course introduces learners to microeconomics in the context of current problems. It explores how market mechanism allocates scare resources among competing uses. It uses supply, demand, production, and distribution theory to analyze problems.

POLI 100 - Contemporary Global Issues

The course addresses contemporary issues impacting international and global affairs, and the major political, social, economic and environmental forces confronting global communities. Some of the themes are democracy and human rights, nationalism and conditions of conflict and stability, economic globalization, resource distribution and depletion, responsibilities of international and transnational organizations, technological development and environmental concerns, cultural diversity and identity, and the possibility of global stability and future world order.

POLI 101 - Politics of Scarcity 

The problems of scarcity and security are as much political as they are economic or technological. This course identifies the political aspects of global economic exchange and distribution, flows of labor and capital, and international cooperation, global security and conflict.

GEOG 100 - (GEOG 200) World Regional Geography 

This course will examine a broad range of geographical perspectives covering all of the major regions of the world. Each region will be reviewed in a similar structure so students can clearly see the similarities and differences between each region. Specifically, the course will explore where each region is located along with its physical characteristics, including absolute and relative location, climate, and significant geographical features. The exploration will then continue on to look at each region from a cultural, economic, and political perspective, closely examining the human impact on each region from these perspectives as well as how human activities impact the environments of the region. The student will first review the basic theories of the discipline of geography, the relationship of world population and resources and the factors affecting development. Next, the student will survey the major regions of the world to identify each region's distinguishing geographic characteristics. This course is a descriptive synthesis of the world's realms and major regions. The basic geographic components of each region, both physical and human, are discussed as the course spans the globe in a single semester to give a broad comparative overview of world regional geography. For each of the world’s realms, a regional issue is identified and current issues will be incorporated into classes as they arise. The aim of this course is to introduce students to the geographic regions of the world while emphasizing the nature of their physical resources, economies, culture and politics. These courses will also address the issue of why certain countries are developed versus under-developed.

COMM 101 - Interpersonal Communication and Group Interaction

Pre-requisite(s): ENGL 101

The course presents the principles to develop appropriate and effective communication strategies in one-to-one and small group communication settings. It emphasizes analyzing and assessing communication skills to create and sustain effective communication in personal and professional relationships.

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.

BIOL 100 - Humankind in a Biological World

Human beings interact with, affect and are affected by other living organisms. This course explores the ways in which human activities have had an impact on other life on earth, mankind and disease and the development of scientific thought.

CHEM 100 - Chemistry in Everyday Life

Co-requisite: CHEM 101

The main focus of this course is on how chemistry is involved our everyday life. It covers the basic chemical principles that impact us with their immediate applications. It addresses the effect of chemicals in everyday life and introduces the techniques that make our lives easier.

CHEM 211 - General Chemistry I 

This course covers the foundations of chemical concepts: basic facts and principles of chemistry, including atoms, molecules, ions, chemical reactions, gas theory, thermochemistry, electrochemistry, chemical kinetics and equilibrium, molecular geometry, and states of matter.

ENVS 102 - Sustainability and Human-Environment Relations

The course examines the interactions between human and environmental systems, and its effect on the future of environmental sustainability. Topics covered include global and local environmental change, conservation of the ecosystem, biodiversity, water management and climate change.

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.

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.

MATH 213 - Calculus III

Pre-requisite(s): MATH 114

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

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.

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.

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.

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.

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 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

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.

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.

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 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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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 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.

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.

CSCI 450 - Information Security and Privacy

Pre-requisite(s): CSCI 215 or Instructor permission

This course is a survey of information security considerations as they apply to information systems analysis, design, and operations. Topics include information security vulnerabilities, threats, and risk management. Furthermore, the course introduces several cryptographic algorithms in addition to the privacy and secrecy of statistical databases and e-government applications.

CSAI 480 - Big Data (3 credits)
Pre-requisite(s): CSCI 326

This course provides an in-depth coverage of various topics in big data from data generation, storage, management, transfer, to analytics, with focus on the state-of-the-art technologies, tools, architectures, and systems that constitute big-data computing solutions in high-performance networks. Real-life big- data applications and workflows in various domains (particularly in the sciences) are introduced as use cases to illustrate the development, deployment, and execution of a wide spectrum of emerging big-data solutions.

CSAI 481 - Computer Vision (3 credits)
Pre-requisite(s): CSAI 450

This course provides an introduction to fundamental topics in computer vision and the application of statistical estimation techniques in this field. It is intended to give students a good basis for work in this important field. Topics include: image representation, image processing, image analysis, image segmentation, object tracking, 3D shape reconstruction, feature detection and tracking, and object detection.

CSAI 482 - Data Mining (3 credits)
Pre-requisite(s): CSAI 350

Data Mining studies algorithms and computational paradigms that allow computers to find patterns and regularities in datasets, then perform prediction/forecasting and generally improve their performance through interaction with data. The course introduces the fundamental concepts of data mining techniques. Topics include data preparation, data classification, cluster analysis, association rule mining, outlier detection, collaborative filtering, and performance measurements.

CSAI 483 - Information Retrieval (3 credits)
Pre-requisite(s): CSCI 326

The course covers basic and advanced techniques for building text-based information systems, including the following topics: efficient text indexing, Boolean and vector-space, retrieval models, evaluation and interface issues, IR techniques for the web, including crawling, link-based algorithms, and metadata usage, document clustering and classification.

CSAI 484 - Internet of Things System (3 credits)
Pre-requisite(s): CSCI 232 and CSCI 462

The course introduces core concepts and networking protocols for IoT applications. Application areas for the Internet of Things with resource-constrained devices (such as sensors and actuators), and networking protocols for collecting sensor data from resource-constrained connected devices to cloud systems, are covered. In this course, students will gain fundamental concepts in the Internet of Things (IoT) networking, and programming of Internet of Things applications, and methods to choose and apply different networking protocols for resource-constrained IoT devices.

CSAI 485 - Introduction to Deep Learning (3 credits)
Pre-requisite(s): CSAI 450

The course provides an introduction to neural networks and deep learning. Topics include the basic conceptual understanding of neural networks, shallow neural networks, radial basis function networks, recurrent neural networks, convolutional neural networks, and deep reinforcement learning. In this course, students will gain foundational knowledge of deep learning algorithms and get practical experience in building deep neural networks.

CSCI 416 - Human Computer Interaction

Pre-requisite(s): CSCI 215

This course provides an introduction to and overview of the field of human-computer interaction (HCI). The topics include usability principles, predictive evaluation, design management processes, graphic design, understanding users’ requirements gathering, task analysis, handling errors & help, prototyping & UI software, interaction styles, user models, evaluation, and universal design.

CENG 431 - Embedded Systems Design

Pre-requisite(s): CENG 315 Co-requisite(s): CENG 432

Introduction to the design of embedded systems. Topics include hardware and software architectures, assembly and C programming, real-time design, interrupts, multitasking, embedded software tools and embedded systems performance. Comprehensive project to design, implement and evaluate a prototype embedded system.

CENG 432 - Embedded Systems Design Lab

Co-requisite: CENG 431

Lab to accompany CENG 431. Labs cover topics such as hardware and software architectures, assembly and C programming, I/O, real-time design, interrupts, embedded systems performance.

CSAI 486 - Special Topics in Artificial Intelligence (3 credits)
Pre-requisite(s): CSAI 350

This course gives instructors the opportunity to cover the latest developments and contemporary issues in technology in the various areas of Artificial Intelligence. Instructors will provide a detailed course outline at the beginning of the semester.

CSAI 487 - Introduction to Robotics (3 credits)
Pre-requisite(s): ECEN 331 and CSAI 350

The course presents an introduction to the field of robotics. It covers the fundamentals of kinematics, dynamics, control of robot manipulators, robotic vision, and sensing. The course deals with forward and inverse kinematics control, the manipulator Jacobian, dynamics, and control. It presents fundamental principles on proximity, tactile, and force sensing, vision sensors, and motion detection.

ENGR 399 - Undergraduate Research Project

Pre-requisite(s): Department Consent

Undergraduate research under the guidance of an engineering faculty member for juniors and seniors. Fixed credit hours; 3 credits are assigned, this is equivalent to a minimum of 9 hours of research time per week; a pass/fail grade is to be used. Student will be engaged in a creative research project at the discretion of the faculty member. The course is open to all engineering students.