Suggested Electives. Students may focus their studies in specific domains within embedded systems by completing groups of electives as shown below.

Embedded System Architectures Focus: CS 250A and CS 250B, CS 242, CS 243, CS 252, CS 253.

Embedded Software Focus: Informatics 211, Informatics 235, CS 243.

Distributed and Networked Embedded Systems Focus: select four of the following five courses: CS 250A, CS 230, CS 232 or CS 236, CS 242.

Micro-Electronic Embedded Systems Focus: CS 251, CS 253, CS 254, CS 255.

System Reliability and Fault Tolerance Focus: CS 250A, CS 251, CS 253.

Theoretical Foundations of Embedded Systems Focus: CS 230, CS 232, CS 258, CS 265.

Comprehensive Examination or Thesis

Each student must either: (1) pass a comprehensive examination administered by the Embedded Systems faculty; or (2) submit a thesis for approval by a three-person committee consisting of an advisor (who is an ICS Embedded Systems full-time faculty member) and two other full-time faculty members (one of which must be from ICS).

M.S. CONCENTRATION IN KNOWLEDGE DISCOVERY IN DATA

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 the past 10 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 new 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.

Required Courses

The following courses must be completed with a grade of B or better: Principles of Data Management (CS 222), Machine Learning (CS 273A), Probabilistic Learning (CS 274A), Data Mining (CS 285), Descriptive Multivariate Statistics I (Informatics 207), and any two courses from the Artificial Intelligence Core Course List and any two courses from the Statistics Core Course List.

For students pursuing the M.S. thesis option, two four-unit courses in Thesis Supervision (CS 298 or Informatics 298) plus any one course from the General Computer Science Course List completed with a grade of B or better.

For students not pursuing the thesis option, the following additional courses must be completed with a grade of B or better: any two courses from the General Computer Science List and one elective. For the elective, a non-ICS course can only be taken with the approval of a KDD faculty member.

Artificial Intelligence Core Course List: Project in Artificial Intelligence (CS 175), Information Retrieval, Filtering, and Classification (CS 221), Introduction to Artificial Intelligence (CS 271), Network-Based Reasoning/Constraint Networks (CS 275), Network-Based Reasoning/Belief Networks (CS 276), Neural Networks (CS 281), Representations and Algorithms for Molecular Biology (CS 284A).

Statistics Core Course List: Discrete Mathematics and Probability (Psychology 203A), Introduction to Mathematical Statistics (Psychology 203B), Experimental Design (Psychology 203C), Theory of Mathematical Statistics (Mathematics 201A), Probability (Mathematics 270A-B-C).

General Computer Science Course List: Project in System Design (Informatics 117), Human Computer Interaction (Informatics 231), Software Engineering (Informatics 211), Software Analysis and Testing (Informatics 215), Advanced User Interface Architectures (Informatics 235), Fundamentals of the Design and Analysis of Algorithms (CS 260), Data Structures (CS 261), Analysis of Algorithms (CS 263), Graph Algorithms (CS 265), Computational Geometry (CS 266).

Comprehensive Examination

All students not pursuing the thesis option must pass a comprehensive examination administered and evaluated by the KDD faculty.

M.S. CONCENTRATION IN ARTS COMPUTATION ENGINEERING (ACE)

As digital technologies infiltrate increasingly diverse aspects of cultural practice, and human culture at large is influenced by the presence of digital technologies, there is a profound need for a new type of professional in the entertainment industry, in education, and in the arts, who can help to construct, manage, and monitor these changes. Such a professional must be technically skilled, artistically skilled, and theoretically skilled, all at an equally high and rigorous level. The goal of the M.S. concentration in Arts Computation Engineering is to provide students with a broad-based and interdisciplinary training at the intersection of digital technology and cultural and artistic practices. The ACE program is coordinated across the Donald Bren School of Information and Computer Sciences, The Henry Samueli School of Engineering, and the Claire Trevor School of the Arts, and places equal emphasis on technical, artistic, and critical proficiency. Strongly practical in composition, it provides students with the opportunity to explore in detail topics such as telematic performance, immersive and augmented environments, embodied interaction, and the cultural impact of new technologies.

Graduation is by publicly presented thesis project and written thesis, in addition to completion of course work.

Required Courses

The ACE concentrations in all three fields consist of a two-year curriculum. The following courses are required:

ACE Core: five ACE interdisciplinary theory seminars (Informatics 270), four ACE studio/labs (Informatics 271-277), two ACE project internships (Informatics 279), and one quarter of ACE thesis research (Informatics 278).

(NOTE: A total of 48 units of Core courses must be completed. Any of the ACE core category courses may be reduced by one and replaced with a different ACE core course or an elective, in consultation with the student's advisor.)

Electives: a minimum of four ICS electives including one from each of the following three categories, passed with a grade of B or better:

Theory: Fundamentals of the Design and Analysis of Algorithms (CS 260), Data Structures (CS 261), Analysis of Algorithms (CS 263);

Architecture/CAD/Hardware: Embedded Computing Systems (CS 244/Informatics 244), Computer Systems Architectures (CS 250A), Internet (CS 232/EECS248A/Networked Systems 201), Introduction to Computer Design (CS 252);

Software Systems: Human-Computer Interaction (Informatics 231), Advanced Compiler Construction (CS 241), Data Management (CS 222), Software Engineering (Informatics 211), Distributed Computer Systems (CS 230).

Two additional breadth electives: that may be chosen by students in consultation with an advisor, and/or may be assigned by the ACE program committee in consultation with the student. These courses will compensate for lacunae in the student's background and may include upper-division undergraduate courses when appropriate and approved in advance by the candidate's advisor.

A program faculty member from the School of ICS will advise on elective selection and may be on the thesis committee.