Department of Computer Science and Engineering




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9/19/2012

THE WPE MEMO 2007- 08

(FALL 2007)

Department of Computer Science and Engineering

University of Minnesota


1. Introduction


This memo describes the Fall 2007 written preliminary examination (WPE) for the PhD program in the Department of Computer Science and Engineering at the University of Minnesota.


Who Can Take This Examination


Only students admitted to the graduate program in Computer Science (MS or PhD) or Computer Engineering MS students may take this examination. MS students who pass this examination may be allowed to apply to the PhD program.


All graduate students who intend to take the WPE must complete the application form attached to this memo. Applications must be submitted in 4-192 EE/CSci by the deadline specified on the form. Students will be permitted to take the WPE only if the WPE Committee accepts their applications.


2. WPE Format


To be accepted into Ph.D. candidancy, students must complete the Breadth requirement and pass the Major exam, which consists of two parts:

  1. In-Class Exam

  2. Take Home Exam


2.1 PhD Breadth Requirements


The Breadth requirement can be satisfied through coursework as detailed below and need not be completed before taking the written part of the WPE:


  1. The required number of Breadth courses is 6. Two courses are required in each of the 3 breadth areas:




    • Theory

    • Systems

    • Applications


The courses in these areas are listed below.


  1. The student should get a B (3.0) or better grade in each of the courses. Students must maintain an overall GPA of 3.25 (for Master's candidates) and 3.45 (for Ph.D. candidates) for all courses on their degree program, including those used to satisfy the breadth requirement. The breadth courses must be listed on the student’s Degree Program.




  1. A student with an MS degree from another University can petition to transfer up to 3 breadth courses, one from each area. Courses used to obtain the MS degree in our CS or CE programs can be reused for the PhD.




  1. For students entering the Ph.D. program with a substantial number of requisite Breadth courses from their previous programs, there would be a provision to substitute advanced courses (including 8xxx level) in place of the Breadth courses listed below. This would be decided on a case-by-case basis by the DGS in consultation with the appropriate faculty. The student will be required to file a petition to make such substitutions.

  2. List of courses:


Note: OR means exclusive-or.


Theory:


5302 Analysis of Numerical Algorithms

5304 Computational Aspects of Matrix Theory

5403 Computational Complexity

5421 Advanced Algorithms and Data Structures

  1. Intro to Parallel Computing: Architecture, Algorithms & Programming

  1. Modern Cryptography

5525 Machine Learning


No more than one of the following Mathematics courses (PhD students only):

MATH 5165 Mathematical Logic

MATH 5707 Graph Theory and Non-enumerative Combinatorics

MATH 5711 Linear Programming and Combinatorial Optimization

EE 5531 Probability and Stochastic Processes


Systems:


5103 Operating Systems

5104 System Modeling and Performance Evaluation

5105 Foundations of Modern Operating Systems

5106 Programming Languages

5131 Advanced Internet Programming (Beginning Fall 2002)

5143 Real-Time and Embedded Systems

5161 Introduction to Compilers

5204 Advanced Computer Architecture

  1. Data Communications and Computer Networks

5271 Introduction to Computer Security

  1. Architecture and Implementation of Database Management Systems



No more than one of the following EE courses (PhD students only):


EE 5323 VLSI Design I

EE 5371 Computer Systems Performance Measurement and Evaluation

EE 5381 Telecommunications Networks (This cannot be counted with CSci 5211)


Applications:


5107/5108 Fundamentals of Computer Graphics I OR II

5109 Visualization

5115/5116 User Interface Design: Implementation and Evaluation OR GUI Tools

  1. Computer Aided Design I

5481 Computational Techniques for Genomics

5511/5512/5519 Artificial Intelligence I OR Artificial Intelligence II

  1. Pattern Recognition

5523 Introduction to Data Mining

5541 Natural Language Processing

5551 Intro to Intelligent Robotic Systems

5552 Sensing and Estimation in Robotics

5561 Computer Vision

5707 Principles of Database Systems

5801/5802 Software Engineering I OR Software Engineering II

No more than one of the following EE courses (PhD students only):


EE 5329 VLSI Digital Signal Processing Systems

EE 5301 VLSI Design Automation I (This cannot be counted with CSci 5283)


These "OR" operators are exclusive-OR: only one of the two classes may be taken to satisfy the breadth requirement in the area. The breadth requirement is specified in this way to ensure that students gain broad exposure within computer science, rather than focusing on a narrow sub-discipline.


2.2 Major In-Class and Take-Home Examinations



Students will be required to discuss with their research advisor or the DGS their research plans and objectives to determine the subjects in which to take the in-class and take-home exams.


The Major exam will have a uniform structure for all students, irrespective of their areas of research. The Major exam will have two parts:


In-Class

  • The student will be taking an in-class exam consisting of two parts on two different subjects from the list in row 1 of Table 1. A student will choose any two subjects in which to answer questions. The subjects selected should be most appropriate for the student’s thesis work. For example, a student can select HCI and Learning, or OS and Networks. The duration of the in-class exam will be 4 hours, with about 2 hours needed for each subject exam, which will be comprehensive.




  • Table 1 consists of a row of in-class questions and a column down the side of take home questions. If there is an X where the two topics intersect, a student can only take either the in-class or the take-home exam. The student should choose, in consultation with his/her advisor, the subject areas that are best related to the student’s research plans.




  • Each subject area exam will be either open-book or closed book. If an in-class exam subject will be offered as an open book exam, only reference books listed in the WPE memo will be permitted. No typed or handwritten notes or any other printed materials will be permitted for any exam and only basic calculators will be allowed. There will be no laptops. PDAs or cell-phones allowed.


Take-Home


  • The student will be required to answer a take-home exam in one of the subjects listed in column 1 of Table 1. A student can take the take-home exam in any one of these subjects, independent of the choice of major area. For instance, a theory student interested in computer vision algorithms may choose to take the Computer Vision take-home exam.




  • The take-home exam will be distributed on a Friday by noon, or immediately after the in-class exam if the in-class exam is held on a Friday. The students will be required to submit their paper by noon on the following Tuesday.




  • The purpose of the take-home exam is to assess the ability of the student to analyze research literature in the subject area or answer questions based on a set of papers.




  • The students will be required to declare the subjects for the in-class and the take-home at the time they register for the WPE. Take-home exams will only be prepared for subjects students have chosen in advance.




I
If there is an X in a box, you cannot take both the in-class and take-home questions.
n-Class


Exam Subjects

Architecture (5204)

CAD (5283)

Compilers (5161)

Data Structures and Algorithms (5421)

Databases (5707)

Graphics (5107)

HCI (5115)

Learning (5512)

Logic, Knowledge Rep, Plan, Search (5511)

Networks (5211)

Numerical Analysis

(5302 and 5304)

Operating Systems (5103)

Programming Languages (5106)

Robotics and Sensing (5551)

Software Engineering (5801)

Take- Home















































Architecture

X











































CAD




X








































Compilers







X





































Computational Geometry














































Computer Vision














































Data Mining














































Databases













X































Distributed Systems


































X










Graphics
















X




























HCI/GUI



















X

























Intelligent Agents














































Math Models for Data Mining














































Networks




























X
















Parallel Algorithms














































Programming Languages





































X







Security/ Cryptography














































Software Engineering











































X

Sparse matrix computations














































Special matrix problems and Advanced matrix computations














































Visualization













































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