Schedule of Classes

Brings together the latest research in statistics, databases, machine learning, and artificial intelligence that are part of the rapidly growing field of knowledge discovery and data mining. Topics covered include fundamental issues, classification and clustering, machine learning algorithms, trend and deviation analysis, dependency modeling, integrated discovery systems, next generation database systems, data warehousing, and OLAP and application case studies. Cross-listed with CS 563.

Design and analysis of algorithms. Dynamic structures maintenance and hashing. Searching, sorting, and traversal. Time and space requirements; simplification; computational complexity; proof theory and testing; NP-hard and NP-complete problems.

Brings together the latest research in statistics, databases, machine learning, and artificial intelligence that are part of the rapidly growing field of knowledge discovery and data mining. Topics covered include fundamental issues, classification and clustering, machine learning algorithms, trend and deviation analysis, dependency modeling, integrated discovery systems, next generation database systems, data warehousing, and OLAP and application case studies. Cross-listed with CS 463. For cross-listed undergraduate/graduate courses, the graduate-level course will have additional academic requirements beyond those of the undergraduate course.

Special software engineering research and development projects under staff supervision. Emphasis on a specific topic and emerging technologies in the software engineering area.

Special projects under staff supervision on advanced problems in numerical or non-numerical branches of computer science. May be taken more than once under different topics for a maximum of 6 semester hours.

Individual study in an area of computer science relevant to the student's professional goals and not covered in a formal course offered by the department. May be repeated twice for a maximum of 6 credit hours.