Graduate Degrees and Concentrations

The UCI General Catalogue is the official guide to all degree and graduation requirements; the information below is intended for general planning purposes only.

Concentration in Knowledge Discovery and Data (KDD), (M.S. only)

The goal of the M.S. concentration in Knowledge Discovery and Data is to educate students in both the fundamental principles of computational methods for modeling data, as well as to provide a broad foundation in emerging methods for knowledge discovery and data mining. more »

Fall 2001 to Current Degree Requirements
Concentration Core:

All courses must be passed with a grade of B or better.

  • Required:
    • CS 222: Principles of Data Management
    • CS 273A: Machine Learning
    • CS 274A: Probalistic Learning: Theory & Algorithms
    • CS 277: Data Mining
    • INF 207: Descriptive Multivariate Statistics I
    • Any two courses from the Artificial Intelligence Core List and any two courses from the Statistics Core List
  • Artifical Intelligence Requirement (choose two of the following):
    • CS 175: Project in Artifical Intelligence
    • CS 221: Information Retrieval, Filtering & Classification
    • CS 271: Introduction to Artifical Intelligence
    • CS 275: Network-Based Reasoning/Constraint Networks
    • CS 276: Network-Based Reasoning/Belief Networks
    • CS 281: Neural Networks
    • CS 284A: Representations and Algorithms for Molecular Biology
  • Statistics Core List(choose two of the following):
    • PSYCH 203A: Discrete Mathematics and Probablity
    • PSYCH 203B: Introduction to Mathematical Statistics
    • PSYCH 203C: Experimental Design
    • MATH 201A: Theory of Mathematical Statistics
    • MATH 270A-B-C: Probability (three course sequence)
General Computer Science::

Students pursuing the Thesis option must enroll in two four-unit courses of CS 298 or INF 298, plus one of the following courses. Students pursuing the comprehensive exam option must choose two of the following courses, plus one ICS elective (a non-ICS course must be approved by a KDD faculty member).

  • Select two :
    • INF 117: Project in Software System Design
    • INF 231: Human Computer Interaction
    • INF 211: Software Engineering
    • INF 215: Software Analysis and Testing
    • INF 235: Advanced User Interface Architecture
    • CS 260: Fundamentals of the Design and Analysis of Algorithms
    • CS 261: Data Structures
    • CS 263: Analysis of Algorithms
    • CS 265: Graph Algorithms
    • CS 266: Computational Geometry

More about the degree...

The goal of the M.S. concentration in Knowledge Discovery and Data is to educate students in both the fundamental principles of computational methods for modeling data, as well as to provide a broad foundation in emerging methods for knowledge discovery and data mining.

Technological advances in digital data collection, memory capacity, and computational power have revolutionized our view of data analysis in recent years. The volumes of data being collected in science, business, medicine, and government are truly vast in nature.

Across all of these areas, there is a rapidly increasing demand for better theories and tools to provide users with improved understanding of their data and to leverage their data for decision support.

Knowledge discovery in databases (KDD) is an emerging discipline within computer science, focused on the principles of how patterns and structure can be inferred from large data sets. It is an area of significant academic interest and research opportunity.

For example, a Special Interest Group in Knowledge Discovery in Databases (SIGKDD) was recently started by the Association for Computing Machinery (ACM) to promote both research and professional activities in this area; a journal called Data Mining and Knowledge Discovery was started in 1997; and the field sponsors an annual international conference with over 500 attendees.

In addition, the National Science Foundation has recently begun a large interdisciplinary research program in Knowledge and Distributed Intelligence (KDI), based in part on recent research and interest in KDD.

Industry participation is also very active with broad demand for graduates in this area, across a wide variety of companies engaged in leveraging scientific and business data for strategic purposes.