Graduate Computer Science
Graduate Computer Science
2026 Spring Term
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MATRIX METHODS IN DATA MINING AND PATTERN RECOGNITION
COMPSCI 739
This course focuses on matrix methods in data mining and pattern recognition, and features real-world applications ranging from classification and clustering to denoising and data analysis. The topics covered include: linear equations, regression, regularization, the singular value decomposition, iterative algorithms, classification using singular value decomposition bases, tangent distance, latent semantic indexing, clustering, support vector machines, and random walk and Markov chains.
CRYPTOGRAPHY AND SECURITY PROTOCOLS
COMPSCI 755
This course focuses on the cryptographic solutions to security issues related to confidentiality, integrity, and authentication in networks. The main contents include block cipher and operations; stream cipher; public key cryptography; cryptography-based security protocols in authentication and key management; network, transport, and application layer security in the Internet; and applications of cryptography on security protocols in emerging fields of computing.
STATISTICAL COMPUTING AND APPLICATIONS
COMPSCI 761
This course will provide students with hands-on experience in analyzing real-world data using various statistical tools. This includes the knowledge of basic probability techniques, probability distribution, regression (linear, logistic, etc.), hypothesis testing, and others. The students will be using the programming language R to formulate and analyze data.
BIG DATA AND DATA MINING
COMPSCI 767
This course will cover two main areas: (1) machine learning algorithms that can be applied to "big data" (i.e., data sets of great size and complexity); and (2) distributed file systems and MapReduce as tools to generate algorithms, along with associated hardware innovations to facilitate parallel analysis of big data.
DEEP LEARNING
COMPSCI 768
This course provides a broad introduction to Deep Learning. Topics include but are not limited to Perceptrons, Feed-forward networks, Convolutional Neural networks and transformers. Particular focus will be on the theoretical understanding of these methods, as well as their practical applications.
ADVANCED SOFTWARE ENGINEERING
COMPSCI 776
This course introduces fundamental software engineering principles and techniques. Students will apply these principles and techniques throughout the course as they work together in teams to develop a software product. Students will also learn about current software engineering research and discuss current issues in the software industry.
CAPSTONE PROJECT
COMPSCI 789
Under faculty supervision, the student will develop, extend, or modify a significant piece of software or a system with significant software components. The student will also write a technical report and give a presentation describing the software product as well as the development process. Fulfills the Applied Research Project option for graduation. Pass/Fail grade basis only.
INTERNSHIP IN COMPUTER SCIENCE
COMPSCI 793
INDIVIDUAL STUDIES
COMPSCI 798
Study of a selected topic or topics under the direction of a faculty member.
THESIS RESEARCH
COMPSCI 799
Guided investigation of an approved thesis topic. Students may receive credit for research activities planned in conjunction with their advisers and leading to completing a master's degree.