MS in Computer Science

Discusses basic methods for designing and analyzing efficient algorithms emphasizing methods used in practice. Topics include sorting, searching, dynamic programming, greedy algorithms, advanced data structures, graph algorithms (shortest path, spanning trees, tree traversals), matrix operations, string matching, NP completeness. Prereq: MET CS248 and either MET CS341 or MET CS342. Or METCS 521 and METCS 526. Or instructor's consent.  [ 4 cr. ]

Overview of operating system characteristics, design objectives, and structures. Topics include concurrent processes, coordination of asynchronous events, file systems, resource sharing, memory management, security, scheduling and deadlock problems. Prereq: MET CS472, and MET CS231 or MET CS232, or instructor's consent.   [ 4 cr. ]

Theory of finite automata and regular expressions and properties of regular sets. Context- free grammars, context-free languages, and pushdown automata. Turing machines, undecidability problems, and the Chomsky hierarchy. Introduction to computational complexity theory and the study of NP-complete problems. Prerequisite: MET CS 248 or instructor's consent.  [ 4 cr. ]

Overview of techniques and tools to develop high quality software. Topics include software development life cycle such as Agile and DevOps, requirements analysis, software design, programming techniques, refactoring, testing, as well as software management issues. This course features a semester-long group project where students will design and develop a real world software system in groups using Agile methodology and various SE tools, including UML tools, project management tools, programming frameworks, unit and system testing tools , integration tools and version control tools.

This course provides a robust understanding of networking. It teaches the fundamentals of networking systems, their architecture, function and operation and how those fundamentals are reflected in current network technologies. Students will learn the principles that underlie all networks and the application of those principles (or not) to current network protocols and systems. The course explains how layers of different scope are combined to create a network. There will be a basic introduction to Physical Media, the functions that make up protocols, such as error detection, delimiting, lost and duplicate detection; and the synchronization required for the feedback mechanisms: flow and retransmission control, etc. Students will be introduced to how these functions are used in current protocols, such as Ethernet, WiFi, VLANs, TCP/IP, wireless communication, routing, congestion management, QoS, network management, security, and the common network applications as well as some past applications with unique design solutions. Prereq: MET CS 575 and MET CS 201 or MET CS 231 or MET CS 232. Or instructor's consent. Restrictions: This course may not be taken in conjunction with MET CS 625 or MET CS 425 (undergraduate). Only one of these courses can be counted towards degree requirements.  [ 4 cr. ]

This course provides a theoretical yet modern presentation of database topics ranging from Data and Object Modeling, relational algebra and normalization to advanced topics such as how to develop Web-based database applications. Other topics covered - relational data model, SQL and manipulating relational data; applications programming for relational databases; physical characteristics of databases; achieving performance and reliability with database systems; object- oriented database systems. Prereq: MET CS 231 or MET CS 232; or instructor's consent. Restrictions: This course may not be taken in conjunction with MET CS 469 (undergraduate) or MET CS 669. Refer to your Department for further details.   [ 4 cr. ]

This course is primarily the study of design of graphic algorithms. At the end of the course you can expect to be able to write programs to model, transform and display 3- dimensional objects on a 2-dimensional display. The course starts with a brief survey of graphics devices and graphics software. 2-d primitives such as lines and curves in 2- d space are studied and a number of algorithms to draw them on a rectangular surface are introduced, followed by a study of polygons, scan conversion and other fill methods. Attributes of the primitives are studied as well as filtering and aliasing. Geometric transformations in 2 dimensions are introduced in homogeneous coordinates, followed by the viewing pipeline, which includes clipping of lines, polygons and text. Hierarchical graphics modeling is briefly studied. The graphics user interface is introduced and various input functions and interaction modes are examined. 3-d graphics is introduced through object representations through polygonal methods, spline techniques, and octrees. This is followed by 3-d transformations and the 3-d viewing pipeline. The course ends with a study of algorithms to detect the visible surfaces of a 3-d object in both the object space and the image space. Laboratory Course. Prereq: MET CS 248 and MET CS 341 or MET CS 342. Or instructor's consent.   [ 4 cr. ]

Formerly titled CS 544 Foundations of Analytics with R.
The goal of this course is to provide students with the mathematical and practical background required in the field of data analytics. Probability and statistics concepts will be reviewed as well as the R tool for statistical computing and graphics. Different types of data are investigated along with data summarization techniques and plotting. Data populations using discrete, continuous, and multivariate distributions are explored. Errors during measurements and computations are analyzed in the course. Confidence intervals and hypothesis testing topics are also examined. The concepts covered in the course are demonstrated using R. Laboratory Course. Prereq: MET CS546 and (MET CS520 or MET CS521), or equivalent knowledge, or instructor's consent.  [ 4 cr. ]

Mathematics is fundamental to data science and machine learning. This course reviews essential mathematical concepts and procedures which are fundamental. These concepts are illustrated by Python and/or R code and by many visualizations. This course discusses mathematical concepts and computational methods for data science using simple self-contained examples, intuition and visualization. These examples will help develop intuitive explanations behind mathematical concepts. Extensive visualizations will be used to illustrate core mathematical concepts. The emphasis is both on mathematics and computational algorithms that are at the heart of many algorithms for data analysis and machine learning. This course will advance students mathematical skills that can be used effectively in data analytics and machine learning. Prerequisite: Basic knowledge of Python or R. Or instructor's consent.  [ 4 cr. ]

Formerly titled CS 555 Data Analysis and Visualization with R.
This course provides an overview of the statistical tools most commonly used to process, analyze, and visualize data. Topics include simple linear regression, multiple regression, logistic regression, analysis of variance, and survival analysis. These topics are explored using the statistical package R, with a focus on understanding how to use and interpret output from this software as well as how to visualize results. In each topic area, the methodology, including underlying assumptions and the mechanics of how it all works along with appropriate interpretation of the results, are discussed. Concepts are presented in context of real world examples. Recommended Prerequisite: MET CS 544 or equivalent knowledge, or instructor's consent.  [ 4 cr. ]

This course presents financial algorithms used in applications of computer science in financial decision analysis, risk management, data mining and market analysis, and other modern business processes. The course covers theoretical background on probabilistic methods used for financial decision making and their application in number of fields such as financial modeling, venture capital decision making, operational risk measurement and investment science. Number of financial applications and algorithms are being presented for portfolio risk analysis, modeling real options, venture capital decision making, etc. The course concludes with algorithms for financial risk assessment and presents the security concepts and challenges of financial information systems.   [ 4 cr. ]

This course is designed for IT professionals, and those training to be IT professionals, who are preparing for careers in healthcare-related IT (Health Informatics). This course provides a high-level introduction into basic concepts of biomedicine and familiarizes students with the structure and organization of American healthcare system and the roles played by IT in that system. The course introduces medical terminology, human anatomy and physiology, disease processes, diagnostic modalities, and treatments associated with common disease processes. IT case studies demonstrate the key roles of health informatics and how IT tools and resources help medical professionals integrate multiple sources of information to make diagnostic and therapeutic decisions.  [ 4 cr. ]

This course presents the fundamental principles, concepts, and technological elements that make up the building blocks of Health Informatics. It introduces the characteristics of data, information, and knowledge in the domain, the common algorithms for health applications, and IT components in representative clinical processes. It presents the conceptual framework for handling biomedical data collection, storage, and optimal use. It covers the concepts of population health and precision medicine and the information systems that support them. It introduces basic principles of knowledge management systems in biomedicine, various aspects of Health Information Technology standards, and IT aspects of clinical process modeling. Students design a simple Health Informatics solution as a term project.  [ 4 cr. ]

Health Information Systems are comprehensive application systems that automate the activities of healthcare delivery including clinical care using electronic health records (EHRs), coordination of care across providers, telehealth, management of the business of healthcare such as revenue cycle management, and population health management. The course covers the functionality of these systems, the underlying information technology they require and their successful operations. It addresses challenges in this rapidly changing field such as complex data, security, interoperability, mobile technology and distributed users. The course emphasizes applied use of health information systems through case studies, current articles, and exercises.  [ 4 cr. ]

In this course we will study the fundamental and design applications of various biometric systems based on fingerprints, voice, face, hand geometry, palm print, iris, retina, and other modalities. Multimodal biometric systems that use two or more of the above characteristics will be discussed. Biometric system performance and issues related to the security and privacy aspects of these systems will also be addressed.   [ 4 cr. ]

This course focuses on building core competencies in web design and development. It begins with a complete immersion into HTML essentially XHTML and Dynamic HTML (DHTML). Students are exposed to Cascading Style Sheets (CSS), as well as Dynamic CSS. The fundamentals of JavaScript language including object-oriented JavaScript is covered comprehensively. AJAX with XML and JSON are covered, as they are the primary means to transfer data from client and server. Prereq: MET CS 200, MET CS 231, MET CS 232 or MET CS 300. Or instructor consent.  [ 4 cr. ]

The Server-Side Web Development course concentrates primarily on building web applications using PHP/MySQL and Node.js/MongoDB. The course is divided into various modules covering in depth the following topics: PHP, MySQL, Object oriented PHP, PHP MVC, Secure Web applications, Node.js and MongoDB. 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 web application development on the server-side. Prerequisite: MET CS 601. Or instructor's consent.  [ 4 cr. ]

Polymorphism, containers, libraries, method specifications, large-scale code management, use of exceptions, concurrent programming, functional programming, programming tests. Java will be used to illustrate these concepts. Students will implement a project or projects of their own choosing, in Java, since some concepts are expressible only in Java. Prerequisite: MET CS 342 or equivalent knowledge of Java. Or MET CS 521 and MET CS 526. Or instructor's consent. Effective Fall 2020, this course fulfills a single unit in each of the following BU Hub areas: Quantitative Reasoning II, Creativity/Innovation, Critical Thinking.  [ 4 cr. ]

This course provides students with a comprehensive overview of the principles, processes, and practices of software project management. Students learn techniques for planning, organizing, scheduling, and controlling software projects. There is substantial focus on software cost estimation and software risk management. Students will obtain practical project management skills and competencies related to the definition of a software project, establishment of project communications, managing project changes, and managing distributed software teams and projects. Effective Fall 2020, this course fulfills a single unit in the following BU Hub area: Teamwork/Collaboration.  [ 4 cr. ]

Theory and practice of security and quality assurance and testing for each step of the software development cycle. Verification vs. validation. Test case design techniques, test coverage criteria, security development and verification practices, and tools for static and dynamic analysis. Standards. Test-driven development. QA for maintenance and legacy applications. From a project management knowledge perspective, this course covers the methods, tools and techniques associated with the following processes -- Plan Quality, Perform Quality Assurance, and Perform Quality Control.   [ 4 cr. ]

The purpose of this course is to provide students with a deeper understanding of Media-specific Technologies not only so that they will be able to use the ones covered in this course, but more importantly be able to analyze and evaluate new technologies. This course applies the principles from CS 535 to understand the engineering that lead to them as well as the special problems that confront network technologies that operate directly over the physical media. These Media specific layers have three problems to solve: the usual one of multiple users of a common resource, accommodating the particular characteristics of the media, and providing (to the degree possible) a media- independent service to the layers above. While CS 535 provides a high-level view of some of these technologies, in this course, they are considered in much greater detail as to how these technologies address their requirements and take advantage of the assumptions made. The emphasis is on those technologies that are either representative of a type or take a unique perspective on the problem. Hence, the traditional data link protocols, such as HDLC, modern Ethernet (primarily VLANs), WiFi (802.11) represent the first type, while media technologies, such as DOCSIS, RFIDs, IoT, and cellular mobile networks are representative of the second. The course will consider how these technologies solve mobility, routing, congestion, QoS (multi-media), security, etc. A major project is part of this course. Prereq: MET CS 231 or MET CS 232 and either MET CS 625 or MET CS 535; or instructor's consent.   [ 4 cr. ]

Study of the ideas and techniques that enable computers to behave intelligently. Search, constraint propagations, and reasoning. Knowledge representation, natural language, learning, question answering, inference, visual perception, and/or problem solving. Laboratory course. Prereq: MET CS 341, MET CS 342, MET CS 520 or MET CS 521. Or instructor's consent.  [ 4 cr. ]

Software design principles, the object-oriented paradigm, unified modeling language; creational, structural, and behavioral design patterns; OO analysis and design; implementation of semester project. Laboratory course. Prereq: (MET CS 526 or MET CS 622) and one of the following (MET CS 341, MET CS 342, MET CS 520, or MET CS 521). Or instructor's consent.  [ 4 cr. ]

The course provides a strong foundation in database security and auditing. This course utilizes Oracle scenarios and step-by-step examples. The following topics are covered: security, profiles, password policies, privileges and roles, Virtual Private Databases, and auditing. The course also covers advanced topics such as SQL injection, database management security issues such as securing the DBMS, enforcing access controls, and related issues. Prereq: MET CS 579 or MET CS 669; or instructor's consent.  [ 4 cr. ]

Students will learn major Python tools and techniques for data analysis. There are weekly assignments and mini projects on topics covered in class. These assignments will help build necessary statistical, visualization and other data science skills for effective use of data science in a variety of applications including finance, text processing, time series analysis and recommendation systems. In addition, students will choose a topic for a final project and present it on the last day of class. Prerequisite: MET CS 521 or equivalent. Or, instructor's consent.   [ 4 cr. ]

This course discusses the principles and issues associated with mobile application development using Android as the development platform. Topics covered will include Android application components (Activities, Services, Content Providers and Broadcast Receivers), ICC (Inter-component Communication), UI design, data storage, asynchronous processing, 2D graphics, and Android security. Students will develop their own apps in Java and/or Kotlin using Android Studio in their semester-long projects. Prior knowledge of Java programming is required. Prerequisite: MET CS 342 OR MET CS 520 OR MET CS 521. Or instructor's consent.  [ 4 cr. ]

This course covers important topics that students need to understand in order to effectively manage a successful cybersecurity and privacy program, including governance, risk management, asset classification and incidence response. Students are first introduced to cybersecurity & privacy policy frameworks, governance, standards, and strategy. Risk tolerance is critical when building a cybersecurity and privacy program that supports business goals and strategies. Risk management fundamentals and assessment processes will be reviewed in depth including the methodology for identifying, quantifying, mitigating and controlling risks. Asset classification and the importance of protecting Intellectual Property (IP) will prepare students to understand and identify protection mechanisms needed to defend against malicious actors, including industry competitors and nation states. Incident Response programs will cover preparation and responses necessary to triage incidents and respond quickly to limit damage from malicious actors.   [ 4 cr. ]

. This course will cover contemporary integrated network management based on FCAPS (Fault, Configuration, Administration, Performance, and Security management) model. The introduction to the course will be an overview of data transmission techniques and networking technologies. The middle part of the course will be on Network Management Model, SNMP versions 1, 2 and 3, and MIBs. In the second part of the course, particular focus and emphasis will be given to current network management issues: various wireless networks technologies (WLAN, WiFi, WiMax), Voice-over-IP, Peer-to-Peer Networks, networking services, Identity Management, and Services Oriented Architecture Management. Prereq: MET CS 535 or MET CS 625. or instructor's consent.  [ 4 cr. ]

Formerly titled CS 688 Web Analytics and Mining.
The Web Mining and Graph Analytics course covers the areas of web mining, machine learning fundamentals, text mining, clustering, and graph analytics. This includes learning fundamentals of machine learning algorithms, how to evaluate algorithm performance, feature engineering, content extraction, sentiment analysis, distance metrics, fundamentals of clustering algorithms, how to evaluate clustering performance, and fundamentals of graph analysis algorithms. Laboratory Course. Prerequisites: MET CS 544, or MET CS 555 or equivalent knowledge, or instructor's consent.  [ 4 cr. ]

This course surveys state-of-the art technologies in DW and Big Data. It describes logical, physical and semantic foundation of modern DW infrastructure. Students will create a cube using OLAP and implement decision support benchmarks on Hadoop/Spark vs Vertica database. Upon successful completion, students will be familiar with tradeoffs in DW design and architecture. Prereq: MET CS 579 or MET CS 669 and either MET CS 520 or MET CS 521. Or instructor's consent.   [ 4 cr. ]

This course will cover advanced network security issues and solutions. The main focus on the first part of the course will be on Security basics, i.e. security services, access controls, vulnerabilities, threats and risk, network architectures and attacks. In the second part of the course, particular focus and emphasis will be given to network security capabilities and mechanisms (Access Control on wire-line and wireless networks), IPsec, Firewalls, Deep Packet Inspection and Transport security. The final portion of the course will address Network Application security (Email, Ad-hoc, XML/SAML and Services Oriented Architecture security. As part of our course review we will explore a number of Network Use Cases. Prereq: MET CS 535 or MET CS 625; Familiarity with OSI and TCP/IP protocol stack; Background-familiarity with binary numbers, prime numbers, binary- hexadecimal-decimal conversions, etc; Familiarity with computer programming concepts; or instructor's consent.  [ 4 cr. ]

Provides a comprehensive understanding of digital forensics and investigation tools and techniques. Learn what computer forensics and investigation is as a profession and gain an understanding of the overall investigative process. Operating system architectures and disk structures are discussed. Studies how to set up an investigator's office and laboratory, as well as what computer forensic hardware and software tools are available. Other topics covered include importance of digital evidence controls and how to process crime and incident scenes, details of data acquisition, computer forensic analysis, e-mail investigations, image file recovery, investigative report writing, and expert witness requirements. Provides a range of laboratory and hands-on assignments either in solo or in teams. With rapid growth of computer systems and digital data this area has grown in importance. Prereq: Working knowledge of windows computers, including installing and removing software. Access to a PC meeting the minimum system requirements defined in the course syllabus.  [ 4 cr. ]

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.  [ 4 cr. ]

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.  [ 4 cr. ]

This course aims to study basic concepts and techniques of data mining. The topics include data preparation, classification, performance evaluation, association rule mining, ?regressions and clustering. We will discuss basic data mining algorithms in the class, and students will practice data mining techniques using Python or R. Prereq: CS 521, and CS 546 and either CS 579 or CS 669. Or instructor's consent.  [ 4 cr. ]

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.  [ 4 cr. ]

Overview of techniques and tools to develop secure software. Focus on the application security. Topics include secure software development processes, threat modeling, secure requirements and architectures, vulnerability and malware analysis using static code analysis and dynamic analysis tools, vulnerabilities in C/C++ and Java programs, Crypto and secure APIs, vulnerabilities in web applications and mobile applications, and security testing. Hands-on lab and programming exercises using current tools are provided and required. Prerequisite: At least two 500- level (or above) programming-intensive computer science courses; or instructor's consent.   [ 4 cr. ]

Formerly titled CS767 Machine Learning
Theories and methods for learning from data. The course covers a variety of approaches, including Supervised and Unsupervised Learning, Neural Nets and Deep Learning, Adversarial Learning, Bayesian Learning, and Genetic Algorithms. Each student focuses on two of these approaches and creates a term project. Laboratory course. Prerequisite: MET CS 521 and either MET CS 622, MET CS 673 or MET CS 682. MET CS 677 is strongly recommended. Or, instructor's consent.  [ 4 cr. ]

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.  [ 4 cr. ]

This course is an introduction to large-scale data analytics. Big Data analytics is the study of how to extract actionable, non-trivial knowledge from massive amount of data sets. This class will focus both on the cluster computing software tools and programming techniques used by data scientists, as well as the important mathematical and statistical models that are used in learning from large-scale data processing. On the tools side, we will cover the basics systems and techniques to store large-volumes of data, as well as modern systems for cluster computing based on Map-Reduce pattern such as Hadoop MapReduce, Apache Spark and Flink. Students will implement data mining algorithms and execute them on real cloud systems like Amazon AWS, Google Cloud or Microsoft Azure by using educational accounts. On the data mining models side, this course will cover the main standard supervised and unsupervised models and will introduce improvement techniques on the model side.
Prerequisite: MET CS 521, MET CS 544 and MET CS 555. Or, MET CS 677. Or, Instructor's consent.   [ 4 cr. ]

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.  [ 4 cr. ]

This course presents the details of information processing in hospitals, hospital information systems (HIS), and more broadly health information systems. It presents the architecture, design, and user requirements of information systems in health care environment. It focuses on Information Technology aspects of Health Informatics specifically addressing the design, development, operation, and management of HIS. The first part of this course covers the introductory concepts including information processing needs, and information management in health care environment. The second part covers detailed description of HIS including hospital process modeling, architecture, quality assessment, and applicable tools. The final part of the course covers management of HIS and related issues and extension of this topic to other health care organizations. The course will have a term project providing students a hands-on experience in design and research of HIS. Prereq: MET CS 580; or instructor's consent.  [ 4 cr. ]

This course builds upon the strong technical foundation of our MSCIS and MSCS curricula, by providing students with the CIO-level management perspective and skills of an enterprise architect, in the context of the technologies that implement those architectures. Current technologies and processes explored in the enterprise architecture context include blockchain, microservices, multimodal/analytic databases, DevOps, SAFe (Scaled Agile Framework), containers/Docker, and some leverage of AI techniques. We cover both the migration of legacy enterprise systems and de novo enterprise architecture development, vendor selection and management, cybersecurity in the enterprise, and complex system integration. Enterprise architecture decisions are presented in the context of the business goals and alignment that are critical for success, given globalization and the reality that "all companies are now technology companies." The course content is rich with case studies that illustrate practical application of enterprise architecture approaches and lessons learned. The course also includes a number of realistic enterprise architecture assignments and an incremental term project with components spanning the course, to provide students with hands on enterprise architecture experience. Students develop the understanding and skills needed to define and implement successful enterprise architectures that provide real strategic and concrete value to organizations, such as substantially reducing IT costs while improving performance, agility and alignment of information technology to business goals. On-campus classrooms follow a "flipped classroom" format, where significant class time is devoted to in-class group workshops. Prereq: MET CS 682. Or strategic IT experience. Or instructor's consent.  [ 4 cr. ]

The course covers the main concepts and principles of cryptography with the main emphasis put on public key cryptography. It begins with the review of integers and a thorough coverage of the fundamentals of finite group theory followed by the RSA and ElGamal ciphers. Primitive roots in cyclic groups and the discrete log problem are discussed. Baby-step Giant-step and the Index Calculus probabilistic algorithms to compute discrete logs in cyclic groups are presented. Naor -- Reingold and Blum -- Blum -- Shub Random Number Generators as well as Fermat, Euler and Miller-Rabin primality tests are thoroughly covered. Pollard's Rho, Pollard's and Quadratic Sieve factorization algorithms are presented. The course ends with the coverage of some oblivious transfer protocols and zero-knowledge proofs. There are numerous programming assignments in the course. Prereq: MET CS 248, or instructor's consent.  [ 4 cr. ]

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  [ 4 cr. ]

This course builds on the material covered in CS 789 Cryptography. It begins with the coverage of commutative rings, finite fields, rings of polynomials, and finding of the greatest common divisor in the ring of polynomials. Irreducible polynomials are discussed. Field extensions and fields Fᴩ [x]/P are thoroughly covered. The main emphasis is put on elliptic curves over Fᴩ and F₂ and the ElGamal cipher on elliptic curves is presented. Block ciphers DES and double and triple DES are introduced. AES and WHIRLPOOL block ciphers and modes of operation are covered. The course continues with the introduction of message integrity and message authentication. In the last part of the course cryptographic hash functions SHA-512 and WHIRLPOOL as well as various digital signatures are introduced. Finally, entity authentication and key management issues are discussed. Prereq: MET CS 789; or instructor's consent.  [ 4 cr. ]