Courses

This course provides an in-depth look into the fundamentals of packet-switched network traffic analysis at the network layer and above as applied to problems in traffic engineering, economics, security, etc. The course will explore the design and integration of analytic tools and techniques into the fabric of the network including: spatial and temporal anomaly detection, origin-destination matrix estimation, application mix determination, deep-packet inspection, fingerprinting, intrusion detection and insider threat mitigation. Finally, the course covers active defense and offensive methods reliant on traffic analysis. Contains a lab component. Prerequisite: CSC 431. (3 units; Spring)

This course provides an in-depth look to define the nature and scope of cyber security incident handling services, including intrusion/incident detection, damage control, service continuity, forensic analysis, service/data restoration, and incident reporting. Material covers policy, planning, operations, and technology issues involved in related cyber incident handling plans; i.e., Business Continuity, Disaster Recovery, and Continuity of Operations. Specific incident types addressed include, natural disasters, denial of service, malicious code, malicious misuse of hardware and firmware, unauthorized access, data compromise and inappropriate use, including insider attacks. Emphasis is given to the detection and analysis of infiltration and exfiltration techniques employed during cyber attacks, thus enabling the incident handler to detect low noise attacks, and to deconstruct particularly insidious attacks. Contains a lab component. Prerequisite: CSC 431 (3 units; Fall)

This course provides an advanced course that focuses on key principles of a constructive approach to secure systems. A brief review of operating systems and computer architecture is provided. Major topics include threat characterization and subversion; confinement; fundamental abstractions, principles, and mechanisms, such as reduced complexity, hierarchical relationships, least privilege, hardware protection, resource management and virtualization, software security, secure system composition, mutual suspicion, synchronization, covert and side-channel analysis, secure metadata, secure operational states, usability, and life cycle assurance. Current developments will include advances in security hardware, components, and systems. This course has heavy reliance on software development and implementation. Prerequisite: CSC 431. (3 units; Spring)

This course provides an introduction to the design, implementation, evaluation and theoretical description of algorithms. Classic algorithms for sorting, searching, graph traversal and other fundamental tasks will be implemented, tested and evaluated. More advanced topics such as problem reductions, hard problems and complexity will be explored. Students will develop experience at selecting and applying algorithms to specific problems. (2 units; Fall)

Investigation of modern cloud computing platforms and the practice of creating scalable software solutions that utilize essential cloud computing products like serverless computing, serverless key-value NOSQL databases, cloud storage, publisher/subscriber messaging, identity access and permissions management, machine learning (ML) integration, etc. Prerequisite: EGR 226. (3 units; Spring)

This course is an introduction to all areas of modern image processing. Image formation and capture, display and compression will be covered. Algorithms from single-pixel and neighborhood operations to higher-level functionalities such as object recognition and scene understanding. Extensions to color and 3D imaging will be discussed. Several projects will offer students the opportunity to learn key topics in detail. (3 units; Spring)

This course covers introductory machine learning topics including supervised and unsupervised learning, linear and logistic regression, support vector machines, neural networks (MLPs, CNNs, RNNs, GANs) and more. Coursework includes instruction and programming assignments in algorithmic implementations and high-level library usage. Students also apply machine learning techniques to a unique research project. Prerequisites: EGR 120 or 121, and one of the following: EGR 305, MAT 353, STA 144, 310, or one additional approved statistic course. (3 units; Fall)

The course commences with an examination of the knowledge discovery process. In particular, the introduction equips students with the strategic thinking skills essential to focus on cutting edge data mining techniques that can be applied in a wide variety of settings, e.g., business, engineering, health care, science, etc. Traditional topics include data mining algorithms and implementation issues, advantages and disadvantages of data mining, and examples of knowledge engineering. Current topics such as ubiquitous, distributed, and spatiotemporal geographic data mining will also be explored. This is a practical hands-on course that culminates in a real-world project implemented via open source tools. Prerequisites: EGR 120 or 121, and one of the following: EGR 305, MAT 353, STA 144, 310, or one additional approved statistics course. (3 units; Fall)

Natural language is ubiquitous, e.g., humans speak and write to communicate, to transfer information, and to document knowledge. Natural Language Processing (NLP) is an integral component in countless information systems requiring advanced manipulation of natural language. In this class, students will be introduced to NLP starting with the concept of understanding words in context and the need for natural language processing in the business environment. The discussion continues with a detailed study of words and is a foundational framework supporting phonetics and speech synthesis. Subsequent topics include concepts of how words are grouped together to form unique grammatical units. The last part of the course, explores solving real-world NLP problems and deals with two key areas: corpus building, feature engineering, and application development. Course material is presented via theory-based lectures, group discussion, and practical labs-a culminating research project will be individually crafted. Prerequisites: EGR 120 or 121, and one of the following: EGR 305, MAT 353, STA 144, 310, or one additional approved statistics course. (3 units; Spring)

This course explores advanced topics in computer vision, focusing on the theory and application of image processing, pattern recognition, and visual data interpretation. Students will study key algorithms and techniques such as object detection, segmentation, feature extraction, and deep learning models for visual data analysis. (3 units)

This course provides the tools to create and critically evaluate data visualizations. Focus will be on statistical graphics, graphics that display statistical data. Additionally, recent advances in the field of information visualization will be covered. Prerequisite: STA 144 or MAT 245. (3 units; Spring, even years)

A course to introduce statistical models of advanced least squares regression and standard ANOVA techniques and extensions to categorical data. Students will conceptually understand linear mixed effects models, log linear and generalized linear models for count data; and survival models for the analysis of lifetime data. In addition, students will apply these models to real data, to discern patterns and conclusions, and present their results. Prerequisite: STA 210, EGR 120, or 121. (3 units; Fall, even years)

A course in application of parametric and nonparametric single and multivariable data analytic techniques to sports. Topics will include: linear regression, correlation, confounding and interactions, variable selection, categorical predictors and outcomes, logistic regression, factor analysis, discriminant analysis, and regression techniques with longitudinal data. Prerequisite: STA 210, EGR 120, or 121. (3 units; Spring, odd years)

This course covers the tools, techniques, and methodologies for managing and analyzing large-scale datasets. Students will learn about distributed data storage systems, such as Hadoop and Spark, and techniques for efficient data processing and analysis. Topics include data visualization, data mining, machine learning applications, and scalable algorithm design. (3 units)