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Vulnerabilities, attacks and current defenses and in-depth look at network security. Threats to computer networks through exploiting weaknesses in network design and protocols are analyzed and protection of data confidentiality, integrity and availability throughout the different network services are explored. Topics covered include cryptographic and authentication systems for data protection, network intrusion detection and forensics technologies, network security devices and access control mechanisms, communication privacy and anonymity, and new developments in Internet and Transport Protocols.

Advanced coverage of machine learning algorithms and applications to computer vision, data mining and social media. Specifically, the course focuses on data pre-processing, feature extraction, supervised/unsupervised learning, graphical model, deep learning, and other advanced machine learning topics. The course will also explore several real-world problems, e.g., visual detection/recognition, topics discovery, social media analytics using machine learning approaches.

Advanced coverage of machine learning algorithms and applications to computer vision, data mining and social media. Specifically, the course focuses on data pre-processing, feature extraction, supervised/unsupervised learning, graphical model, deep learning, and other advanced machine learning topics. The course will also explore several real-world problems, e.g., visual detection/recognition, topics discovery, social media analytics using machine learning approaches.

Fundamental knowledge for capturing and analyzing large-scale data from diverse fields including human behavior, sensors, biological signals, and finance. The course introduces platforms for data storage systems and distributed processing of large datasets, covering big data pipeline concepts: collection, storage, ingestion, processing, analytics, and visualization. Students will work with platforms such as AWS Athena, AWS Glue, Kinesis, Elasticsearch, Kibana, Hadoop HDFS and MapReduce, and Spark.

The relational, hierarchical, and network approaches to database systems, including relational algebra and calculus, data dependencies, normal forms, data semantics, query optimization, and concurrency control on distributed database systems.

Prerequisites: Completion of three core courses.   Development of a detailed, significant project in computer science under the close supervision of a faculty member, perhaps as one member of a student team. This project may be a software implementation, a design effort, or a theoretical or practical written analysis. Project report with optional oral presentation must be evaluated by three faculty members including the project supervisor.  

Offered as needed to present advanced material to graduate students.

Prerequisite: Completion of three core courses. Research leading to submission of a formal thesis. This course provides a thesis experience, which offers a student the opportunity to work on a comprehensive research topic in the area of computer science in a scientific manner. Topic to be agreed in consultation with a supervisor. A written thesis must be completed in accordance with the rules of the Graduate School and the College of Engineering. Graded A-F.