MS in Computer Science: A Rigorous, Comprehensive Curriculum

Establish a solid research foundation and explore the basic concepts of data structures and mathematical principles in this introductory computer science course for graduate-level students. Major modules include data structures and discrete mathematics.

Examine the four main programming paradigms: imperative, object-oriented, functional, and logical. Explore language syntax, semantics, names/scopes, types, expressions, assignment, subprograms, abstraction and inheritance.

Explore the concepts that underlie intelligent systems. Topics include problem solving with search, constraint satisfaction, adversarial games, knowledge representation and reasoning using propositional and first order logic, reasoning under uncertainty, introduction to machine learning, automated planning, reinforcement learning and natural language processing.

Study fundamental topics in algorithm design and analysis. Topics include amortized analysis, NP-completeness and reductions, dynamic programming, advanced graph algorithms, string algorithms, geometric algorithms, and local search heuristics.

Gain an understanding of computer networks and the Internet. Explore topics that include http, ftp, e-mail, DNS, socket programming, UDP, TCP, reliable data transfer, IP, routing, NAT, taxonomy and the Ethernet.

Explore basic issues in file processing and database management systems, including physical data organization, relational databases, database design, relational query languages, SQL, query optimization, database integrity and security, object-oriented databases, and object-oriented query languages.

Study the process of discovering interesting knowledge from large amounts of data stored either in databases, data warehouses, or other information repositories. Topics covered include data warehouse and OLAP technology, data preprocessing, data mining primitives, languages, system architectures, mining association rules, classification and prediction, cluster analysis, and mining complex types of data.

High performance data and computing (HPDC) leverages parallel processing in order to maximize performance, speed and throughput of applications. This hands-on course will cover theoretical and practical aspects of HPDC. Theoretical concepts covered include computer architecture, parallel programming, HADOOP, General Purpose GPU (GPGPU) computing and performance optimization. Practical applications will be discussed from various information and scientific fields. Practical considerations will include HPDC Architecture, job scheduling/management and Linux scripting. Regular assignments will try to enhance theoretical concepts introduced in the class through the use of the HPDC cluster.

Learn the basics of Android programming and explore a wide range of security issues and solutions concerning mobile platforms, including permission analysis, textual artifacts analysis, malware analysis, program analysis, and UI analysis.

Gain a broad understanding of data privacy, with topics that include crypto-based solutions for privacy, hiding data from the database user, hiding access patterns from the database owner, anonymous routing and TOR, privacy in online social networks, cellular and Wi-Fi networks, location privacy, privacy in e-cash systems, privacy in e-voting, and genomic privacy.

An introduction to the information processing and computational algorithms that underlie perception. The course focuses on vision and audition but also covers other senses and various types of perceptual processing in biological systems.

Examine the four main programming paradigms: imperative, object-oriented, functional, and logical. Explore language syntax, semantics, names/scopes, types, expressions, assignment, subprograms, abstraction and inheritance.

Explore the concepts that underlie intelligent systems. Topics include problem solving with search, constraint satisfaction, adversarial games, knowledge representation and reasoning using propositional and first order logic, reasoning under uncertainty, introduction to machine learning, automated planning, reinforcement learning and natural language processing.

Study fundamental topics in algorithm design and analysis. Topics include amortized analysis, NP-completeness and reductions, dynamic programming, advanced graph algorithms, string algorithms, geometric algorithms, and local search heuristics.

Gain an understanding of computer networks and the Internet. Explore topics that include http, ftp, e-mail, DNS, socket programming, UDP, TCP, reliable data transfer, IP, routing, NAT, taxonomy and the Ethernet.

Explore basic issues in file processing and database management systems, including physical data organization, relational databases, database design, relational query languages, SQL, query optimization, database integrity and security, object-oriented databases, and object-oriented query languages.

Study the process of discovering interesting knowledge from large amounts of data stored either in databases, data warehouses, or other information repositories. Topics covered include data warehouse and OLAP technology, data preprocessing, data mining primitives, languages, system architectures, mining association rules, classification and prediction, cluster analysis, and mining complex types of data.

High performance data and computing (HPDC) leverages parallel processing in order to maximize performance, speed and throughput of applications. This hands-on course will cover theoretical and practical aspects of HPDC. Theoretical concepts covered include computer architecture, parallel programming, HADOOP, General Purpose GPU (GPGPU) computing and performance optimization. Practical applications will be discussed from various information and scientific fields. Practical considerations will include HPDC Architecture, job scheduling/management and Linux scripting. Regular assignments will try to enhance theoretical concepts introduced in the class through the use of the HPDC cluster.

Learn the basics of Android programming and explore a wide range of security issues and solutions concerning mobile platforms, including permission analysis, textual artifacts analysis, malware analysis, program analysis, and UI analysis.

Gain a broad understanding of data privacy, with topics that include crypto-based solutions for privacy, hiding data from the database user, hiding access patterns from the database owner, anonymous routing and TOR, privacy in online social networks, cellular and Wi-Fi networks, location privacy, privacy in e-cash systems, privacy in e-voting, and genomic privacy.

An introduction to the information processing and computational algorithms that underlie perception. The course focuses on vision and audition but also covers other senses and various types of perceptual processing in biological systems.