Classes I Took At the University of Michigan
in Computer Science and Generative Linguistics
LING 415 Generative Syntax
Instructor: Michael Degraff (now at MIT).
The Principles-&-Parameters approach to syntactic theory,
most prominently brought forward by Chomsky and his colleagues,
aims at representing certain areas of linguistic knowledge as
'mathematical-like' proof systems consisting of formal rules. In
theory, these rules fall under two components of grammar: the
principles and the parameters. The former are innate, thus
universal, and form the common grammatical basis from which every
language is learned. As of the parameters, their settings are
deduced on a language-by-language basis, from the linguistic data
available to the language learner from its particular environment
- such parameters would be, inter alia, what account for the
diversity exhibited among languages of the world. This course
will introduce the Principles-&-Parameters framework, along
with its particular modes of argumentation and linguistic
analysis. We will explore syntactic and semantic regularities
from a number of languages, and consider how these
generalizations can be expressed using 'principles' and
'parameters'.
EECS 303 - Discrete structures
Instructor: Bill Rounds
Fundamental concepts of algebra; partially ordered sets,
lattices, Boolean algebras, semi-groups, rings, polynomial rings.
Graphical representation of algebraic systems; graphs, directed
graphs. Application of these concepts to various areas of
computer engineering.
EECS 487: Interactive
Computer Graphics
Graphics devices and fundamentals of operation. Two
dimensional and three dimensional transformations. Interactive
graphical techniques and applications, three dimensional
graphics, perspective transformation, hidden line elimination.
Data structures and languages for graphics. Interactive graphical
programming.
EECS 492 Introduction to
Artificial Intelligence
Instructor: Michael Wellman
The purpose of this course is to introduce the student to the
major ideas and techniques of Artificial Intelligence, as well as
to develop an appreciation for the engineering issues underlying
the design of intelligent computational agents. The successful
student will finish the course with specific modeling and
analytical skills (e.g., search, logic, probability), knowledge
of many of the most important knowledge representation,
reasoning, and machine learning schemes, and a general
understanding of AI principles and practice. The course will
serve to prepare the student for further study of AI, as well as
to inform any work involving the design of computer programs for
substantial application domains.
EECS 543 Knowledge Based Systems
Instructor: Ed Durfee
Knowledge-based systems (also called just knowledge systems) are
sophisticated AI programs that solve complex problems. These
programs have found considerable use in industry, and are finding
exciting new applications in cyberspace (e.g., as intelligent
agents). Knowledge-based systems are particularly suited to these
kinds of applications because they help users define and modify
knowledge for tasks in a way that users are comfortable with, and
then use this knowledge based on complex inference techniques.
The goal of this class is provide students with an understanding
of the principles and system-building experience needed to create
a knowledge system. These principles are important to any
intelligent system. The course will cover a variety of topics
such as: search methods, functional (lisp) programming,
rule-based programming, logic programming, agent programming, and
advanced problem-solving techniques for domains such as those
involving classification, diagnosis, and design.
EECS 592 Advanced Artificial Intelligence
Instructor: Bill Birmingham
The purpose of this course is to provide a foundation for AI
research by introducing students to the fundamental ideas,
issues, literature, and lore of the field. We focus on the core
areas of AI: knowledge representation, reasoning, planning,
design, problem solving and notions of agency? (omitting the
"peripheral" topics of robotics, vision, and natural
language, for example, as well as the core area of machine
learning). Our method will be to read and critically examine AI
research papers. Each class will cover one or two papers,
selected because of its coverage, significance, or exemplariness.
Critical examination will consist of short written analyses and
in-class discussions of these papers.
EECS 595 Natural Language
Processing
Instructor: Michael Degraff (now at MIT).
A survey of syntactic and semantic theories for natural language
processing, including unification-based grammars, methods of
parsing, and a wide range of semantic theories from artificial
intelligence as well as from philosophy of language. Programming
will be optional, though a project will normally be required.
Courses in Architecture (Selected)
ARCH 431 COMPUTE PROG ARCH
Instructor: Jim Turner
An introduction to the C programming language. Material covered
includes elementary program structuring, program logic, basic
algorithms, interactive programming, data structures, debugging
and program documentation. Course material is presented in weekly
lectures and 6-7 programming assignments.
ARCH 541 2D CMP
GRAPH&MODL
Instructor: Jim Turner
An introduction to the hardware, software, data-structures,
mathematics, and algorithms underlying computer graphics,
computer modeling, and computer-aided design. Topics include:
data-structures for two-dimensional geometries, coordinate
transformations, interactive sketching, operations and
measurements on lines and polygons, polygon set-operations and
windowing environments.
ARCH 509 Rhetoric and
Visual Form
Instructor: Rod Parker (Now becoming an MD)
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ARCH 509 Architectural
Theory: From Vitruvius to Venturi
Instructor: Anatole Senkivetch
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ARCH 812 Theory in
Architectural Research
Instructor: Emmanuel-George Vakalo
This course is intended to provide a foundation to the conduct of
inquiry in architecture. It consists of two parts. The first part
is an introduction to the philosophy of knowledge with an
emphasis on architecture. The second part is a critical review
and evaluation of a broad range of theoretical and methodological
perspectives within the field of architecture. This second part
will investigate alternative approaches to the generation and
acquisition of knowledge commonly used in the four specialization
areas in the Program. An important part of this investigation is
an exploration of perspectives that unify the separate
specialization areas into an integrated discipline of
architecture. A conceptual organization to the field of
architectural research is presented in addition to the major
issues faced by researchers and the research methods used to deal
with these issues. Students are expected to write a series of
papers that review critically the readings and discussions.
ARCH 823 Seminar in
Architectural History and Theory
Instructor: Anatole Senkivetch
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ARCH 813 Research Design
and Methods in Architecture
Instructor: Linda Groat
In this course, students are required to inves-tigate
systematically alternative approaches to research design and
become knowledgeable about a broad range of research methods and
techniques used frequently in architectural research. The
objective of this course is to impart the knowledge and skills in
research design and research methods that are often missing from
undergraduate and graduate architectural education. Upon
completion of this course, students will have an enhanced
understanding of methodological issues in architectural research
and will be better able to select subsequent courses related to
their research interest(s). Examples of topics included in this
course are: gaming/simulation, survey research, historiography,
participant observation, etc.
ARCH 839 Research
Practicum
This is an independent research project that is undertaken and
completed under the supervision of the student's major advisor
and one other Doctoral Program faculty member.
© Samir Emdanat 1997
Last updated: 03/25/98 by [emdanat@umich.edu].
The top level URL for this page is http://www-personal.umich.edu/~emdanat