Bachelor of Science in Artificial Intelligence

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.

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.

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

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

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.

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.

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.

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.

Pre-requisite(s): CSCI 215 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.

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.

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.

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.

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.

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.

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.

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.

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.

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

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 and BAIDS curricula. The recognition of the ethical and legal principles are also aspects of the course.

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 and BAIDS curricula. The recognition of the ethical and legal principles are also aspects of the course.

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.

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.

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.

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 user's requirements gathering, task analysis, handling errors & help, prototyping & UI software, interaction styles, user models, evaluation, and universal design.

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.

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.

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.

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.

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.

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.

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.

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.

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.