Computer Science

• MET CS 694: Mobile Forensics and Security
  Overview of mobile forensics investigation techniques and tools. Topics include mobile forensics procedures and principles, related legal issues, mobile platform internals, bypassing passcode, rooting or jailbreaking process, logical and physical acquisition, data recovery and analysis, and reporting. Provides in-depth coverage of both iOS and Android platforms. Laboratory and hands-on exercises using current tools are provided and required.
• MET CS 695: Cybersecurity
  Undergraduate Prerequisites: (METCS625) or instructor's consent - This course introduces fundamental concepts, principles of cybersecurity and their use in the development of security mechanisms and policies. Topics include basic risk assessment and management; basic legal and ethics issues, various cyber attacks, defense methods and tools; security principles, models and components; different crypto protocols, techniques and tools, including symmetric and asymmetric encryption algorithms, hashing, public key infrastructure, and how they can be used; security threats and defense to hardware, operating systems, networks and applications in modern computing environments. Hands-on labs using current tools are provided and required. Prerequisite: METCS535 or METCS625 or instructor's consent.
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• MET CS 699: Data Mining
  Prerequisites: MET CS 521 & MET CS 546; MET CS 579 or MET CS 669; or consent of instructor. - Study basic concepts and techniques of data mining. Topics include data preparation, classification, performance evaluation, association rule mining, regression and clustering. Students learn underlying theories of data mining algorithms in the class and they practice those algorithms through assignments and a semester-long class project using R. After finishing this course, students will be able to independently perform data mining tasks to solve real-world problems.
• MET CS 701: Rich Internet Application Development
  Undergraduate Prerequisites: MET CS 520 or MET CS 601 and programming experience, or instructor's c onsent - The Rich Internet Application (RIA) Development course concentrates primarily on building rich client web applications in the browser for desktop and mobile devices. The course is divided into various modules covering in depth the following technologies: HTML5, AngularJS, and Ionic framework. Along with the fundamentals underlying these technologies, several applications will be showcased as case studies. Students work with these technologies starting with simple applications and then examining real world complex applications. At the end of this course, students would have mastered the latest and widely used RIA methodologies. Course Prerequisites: METCS520 (Information Structures) and METCS601 (Web Application Development), or instructor's consent.
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• MET CS 767: Advanced Machine Learning and Neural Networks
  Graduate Prerequisites: MET CS 521; MET CS 622, MET CS 673 or MET CS 682; MET CS 677 strongly recommended; or consent of instructor. - Theories and methods for learning from data. The course covers a variety of approaches, including Supervised and Unsupervised Learning, Regression, k-means, KNN’s, Neural Nets and Deep Learning, Recurrent Neural Nets, Rule-learning, Adversarial Learning, Bayesian Learning, and Genetic Algorithms. The underpinnings are covered: perceptrons, backpropagation, attention, and transformers. Each student focuses on two of these approaches and creates a term project.
• MET CS 775: Advanced Networking
  Graduate Prerequisites: (METCS535) or consent of the instructor - This seminar course provides a strong foundation in networking and Internet architecture, data transfer protocols, including TCP, SCTP, QUIC, and IPv6, and a deep look at network resource allocation with an emphasis on protocol- independent hardware for Deep Packet Inspection (DPI) and congestion management. The course goes into greater depth of current topics such as: naming and addressing, synchronization, congestion management and resource allocation (routing) and how they manifest in different environments. There will be assigned readings from the professor that require considerable class participation, both in presenting material and discussing it.
  
  Prereq: MET CS 535 OR MET CS 625,or instructor's consent required.
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• MET CS 779: Advanced Database Management
  Graduate Prerequisites: (METCS579 OR METCS669) or consent of the instructor - This course covers advanced aspects of database management including normalization and denormalization, query optimization, distributed databases, data warehousing, and big data. There is extensive coverage and hands on work with SQL, and database instance tuning. Course covers various modern database architectures including relational, key value, object relational and document store models as well as various approaches to scale out, integrate and implement database systems through replication and cloud based instances. Students learn about unstructured "big data" architectures and databases, and gain hands-on experience with Spark and MongoDB. Students complete a term project exploring an advanced database technology of their choice. Prereq: MET CS 579 or MET CS 669; or instructor's consent.
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• MET CS 782: IT Strategy and Management
  Undergraduate Prerequisites: Restrictions: Only for MS CIS students. - This course describes and compares contemporary and emerging information technology and its management. Students learn how to identify information technologies of strategic value to their organizations and how to manage their implementation. The course highlights the application of I.T. to business needs. CS 782 is at the advanced Masters (700) level, and it assumes that students understand IT systems at the level of CS 682 Systems Analysis and Design. Students who haven't completed CS 682 should contact their instructor to determine if they are adequately prepared. Prereq: MET CS 682, or instructor's consent.
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• MET CS 787: Adversarial Machine Learning
  Prerequisites: MET CS 767 or knowledge of Neural Networks or instructor’s consent. - This course is designed to provide students with a comprehensive understanding of the inherent vulnerabilities/security issues associated with integrating machine learning into various applications, as well as the knowledge/skills to defeat those vulnerabilities. Topics include an overview of categories of attacks against machine learning models and a detailed exploration of adversarial attacks, data poisoning attacks, membership inference attacks, model stealing attacks, as well as various defense solutions against the above-mentioned attacks. Upon the completion of the course, students are expected to know the threats and vulnerabilities that machine learning models face, along with the strategies and tools used to mitigate those risks. Hands-on labs based on existing tools are provided and required. 
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• MET CS 790: Computer Vision in AI
  Prerequisites: MET CS 566 or instructor’s consent. - Students enrolled in this course will gain comprehensive insights into fundamental and advanced concepts within the dynamic realm of computer vision. The curriculum will focus on cutting-edge applications of deep neural networks in computer vision. Through hands-on experiences and practical exercises, students will learn to leverage computer vision and machine learning techniques to solve real-world challenges. This course not only equips students with theoretical knowledge but empowers them to apply these concepts effectively, fostering a deep understanding of how computer visio can be harnessed to address complex problems in diverse industries
• MET CS 793: Special Topics in Computer Science
  Fall 2023 Topic: Generative AI
  
  This course focuses on recent advances in generative AI. It starts by reviewing statistics and regression models related to generative models, then common deep learning methods described. Later, models for designing new content, such as images, music, or text, will be explored, including GAN, VAE, Autoregressive and Diffusion Models. MLP, CNN, RNN, and Transformer models covered in CS 767 are reviewed. Students should be fluent in Python programming and CS 555 and CS 677
• MET CS 795: Directed Study
  Prereq: Consent of advisor. Requires prior approval of student-initiated proposal. Independent study on special projects under faculty guidance.
  
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