COMPUTER AND INFORMATION SCIENCE (EG) {CIS}
COMPUTER SCIENCE & ENGINEERING (CSE)
Undergraduate Courses
099. Undergraduate Research/Independent Study. (C) A maximum of 2 c.u. of CSE 099 may be applied toward the B.A.S. or B.S.E. degree requirements. An opportunity for the student
to become closely associated with a professor (1)
in a research effort to develop research skills and
techniques and/or (2) to develop a program of independent
in-depth study in a subject area in which the professor
and student have a common interest. The challenge
of the task undertaken must be consistent with the
student's academic level. To register for this course,
the student must submit a detailed proposal, signed
by the independent study supervisor, to the SEAS
Office of Academic Programs (111 Towne) no later
than the end of the "add" period.
L/R 110. Introduction to Computer Programming (with Java, for beginners). (C) How do you program computers to accomplish tasks? How do you break down a complex
task into simpler ones? CSE 110 is a "Java lite" course
that covers the fundamentals of object-oriented programming
such as objects, classes, state, methods, loops,
arrays, inheritance, and recursion using the Java
programming language.
112. (PPE 112) Networked Life. (C) How does Google find what you're looking for... and exactly how do they make
money doing so? What properties might we expect any
social network (such as the Penn Facebook) to reliably
have, and are there "simple" explanations
for them? How does your position in a social or economic
network (dis)advantage you, and why? What might we
mean by the economics of spam? What do game theory
and the Paris subway have to do with Internet routing?
Networked Life looks at how our world is connected
-- socially, economically, strategically and technologically
-- and why it matters.
L/R 120. Programming Languages and Techniques I. (C) This will be a fast-paced introduction to the fundamental concepts of programming,
with Java as the main experimental vehicle. We assume
some previous programming experience at the level
of a high school computer science class. If you got
at least 4 in the AP Computer Science A or AB exam,
you will do great. However, we do not assume you
know Java. Basic experience with any programming
language (for instance C, C++, VB, PHP, Perl, or
Scheme) will be sufficient. A quiz will be given
in the second week of class to test your programming
knowledge so that you can decide whether the class
is for you. If you have never programmed before,
you should take CIS 110 first. We will mainly use
Java and the DrJava programming environment, but
we will also experiment with Python, a higher-level
language.
L/R 121. Programming Languages and Techniques II. (B) Prerequisite(s): CIS 120, CIS 260 is a pre or corequisite for but will be strictly a pre-requisite effective Fall 2009. This
is a course about Algorithms and Data Structures
using the JAVA programming language. We introduce
the basic concepts about complexity of an algorithm
and methods on how to compute the running time of
algorithms. Then, we describe data structures like
stacks, queues, maps, trees, and graphs, and we construct
efficient algorithms based on these representations.
The course builds upon existing implementations of
basic data structures in JAVA and extends them for
the structures like trees, studying the performance
of operations on such structures, and their efficiency
when used in real-world applications. A large project
introducing students to the challenges of software
engineering concludes the course.
125. (EAS 125) Technology and Policy. Have you ever wondered why sharing music and video generates such political
and legal controversies? Is information on your PC
safe and should law enforcement be able to access
information you enter on the Web? Will new devices
allow tracking of your every move and every purchase?
CIS 125 is focused on developing an understanding
of existing and emerging technologies, along with
the political, societal and economic impacts of those
technologies. The technologies are spread across
a number of engineering areas and each of them raise
issues that are of current concern or are likely
to be a future issue.
140. (COGS001, LING105, PHIL044, PPE 140, PSYC107) Introduction to Cognitive Science.
(A)Prerequisite(s): An introductory course in Computer Science, Linguistics, Neuroscience,
Philosophy or Psychology. How do minds work? This
course surveys a wide range of answers to this question
from the disciplines ranging from philosophy to neuroscience.
The course devotes special attention to the use of
simple computational and mathematical models. Topics
include perception, action, thought, learning, memory
and social interaction.
240. Introduction to Computer Architecture. (A) Prerequisite(s): CIS 110 or equivalent experience. You know how to program,
but do you know how computers really work? How do
millions of transistors come together to form a complete
computing system? This bottom-up course begins with
transistors and simple computer hardware structures,
continues with low-level programming using primative
machine instructions, and finishes with an introduction
to all aspects of computer systems architecture and
serves as the foundation for subsequent computer
systems courses, such as Digital Systems Organization
and Design (CIS 371), Computer Operating Systems
(CIS 380), and Compilers and Interpreters (CIS 341).
The course will consider the SPARC architecture,
boolean logic, number systems,and computer arithmetic;
macro assembly language programming and subroutine
linkages; the operating system interface and input/output; understanding the output of the C compiler; the use of the C programming language
to generate specific assembly language instructions.
L/R 260. Mathematical Foundations of Computer Science. (B) What are the basic mathematical concepts and techniques needed in computer science?
This course provides an introduction proof principles
and logics, functions and relations, induction principles,
combinatorics and graph theory, as well as a rigorous
grounding in writing and reading mathematical proofs.
L/R 261. Discrete Probability, Stochastic Processes, and Statistical Inference.
(B) Prerequisite(s): CSE 260 or equivalent. This course tightly integrates the theory and applications of discrete probability,
discrete stochastic processes, and discrete statistical inference in the study of computer science. The course
will introduce the Minimum Description Length Paradigm to unite basic ideas about randomness, inference and computation.
Students will be expected to use the Maple programming environment in homework exercises which will include numerical
and symbolic computations, simulations, and graphical displays.
L/R 262. Automata, Computability, and Complexity. (A) Prerequisite(s): CSE 260. The course provides an introduction to the theory of computation. The treatment
is mathematical, but the point of view is that of Computer Science. Broadly speaking, the theory of computation consists
of three overlapping subareas: (1) formal languages and automata; (2) computability and recursive function theory;
(3) complexity theory. The course will focus mostly on (1) and (2). The topics covered include finite automata
and regular languages, context-free languages, Turing machines, Church's Thesis, undecidability, reducibility and
completeness, time complexity and NP- completeness.
277. Introduction to Computer Graphics Techniques. (C) Prerequisite(s): CIS 120. This course is focused on programming the essential geometric and mathematical
concepts underlying modern computer graphics. Using 2D and 3D implementations, it covers fundamental topics
on scene graphs, computational geometry, graphics algorithms, and user interface design. Programming languages
introduced include C++, OpenGL, FLTK and Python.
320. Introduction to Algorithms. (B) Prerequisite(s): CSE 120, 121, 260, 262. How do you optimally encode a text file? How do you find shortest paths in a
map? How do you desgin a communication network? How do you route data in a network? What are the limits
of efficient computation? This course gives a comprehensive introduction to design and analysis of algorithms,
and answers along the way to these and many other interesting computational questions. You will learn about problem-solving;
advanced data structures such as universal hashing and red-black trees; advanced design and analysis
techniques such as dynamic programming and amortized analysis; graph algorithms such as minimum spanning trees and
network flos; NP-completeness theory; and approximation algorithms.
330. Design Principles of Information Systems. (A) Prerequisite(s): CSE 121 and 260. Introduction to database management systems and principles of design. The Entity-Relationship
model as a modeling tool. The relational model: formal languages, the industry standard SQL, relational
design theory, query optimization. Storing and querying XML data. Datalog and recursive queries. Views and data
integration. Overview of system level issues: physical data organization, indexing techniques, and transactions. Connecting
databases to the Web. Course work requires programming in several different query languages, several written
homeworks and a team project.
334. Advanced Topics in Algorithms. (M) Prerequisite(s): CSE 320. Can you check if two large documents are identical by examining a small number
of bits? Can you verify that a program has correctly computed a function without ever computing the function?
Can students compute the average score on an exam without ever revealing their scores to each other? Can you
be convinced of the correctness of an assertion without ever seeing the proof? The answer to all these questions is
in the affirmative provided we allow the use of randomization. Over the past few decades, randomization has emerged as
a powerful resource in algorithm desgin. This course would focus on powerful general techniques for designing
randomized algorithms as well as specific representative applications in various domains, including approximation
algorithms, cryptography and number theory, data structure design, online algorithms, and parallel and distributed
computation.
340. Problem Solving and Programming. (M) Prerequisite(s): CSE 120, 121. This course is about the principles of programmng languages. It studies programming
language concepts by implementing a sequence of interpreters, compilers, and type checkers, each
one introducing a new language concept. The goal of this course is threefold: By studying the concepts and abstractions
of high-level programming languages, students should be able to use them more effectively. Second, by learning how
the features of high-level programming languages are implemented, students should be able to program more expressively
in low-level languages. Finally, by understanding the principles behind programming language design, students should
be able to create, evaluate and compare programming languages.
341. Compilers and Interpreters. (M) Prerequisite(s): Two semesters of programming courses, e.g., CSE 120-121,and CSE 240. You know how to program, but do you know how to write programs that understand
and generate other programs? This is the focus of CSE 341. In addition to traditinal programming language
implementation topics (such as lexing, parsing, grammars, symbol tables, code generation, optimization, garbage collection,
and object-oriented implementation), this course also explores the more general problem of reasoning
about computation (e.g., for detecting bugs or security constraint violations). CSE 341 includes a substantial
and rewarding Java programming project to develop a fully operational compiler for a Java-like object oriented
programming language.
350. Software Design/Engineering. (M) Prerequisite(s): CSE 240. Large systems versus small programs. Problems of scale. Software life-cycle:
design phase, implementation phase, testing, maintenance. Software re-use. Tools/Toolkits/Libraries. Programming
as a group activity. Support tools, e.g., SCCS and RCS. Standards. Software readability and structure. Reading code. Style
sheets. Software Testing: role in process, test cases, testers. Documentation. Embedded documentation and external
documentation.
371. Computer Organization and Design Lab. (B) Prerequisite(s): CIS 240. This is the second computer oganization course and focuses on computer hardware
design. Topics covered are: (1) basic digital system design including finite state machines, (2) instruction
set design and simple RISC assembly programming, (3) quantitative evaluation of computer performance, (4) circuits
for integer and floating-point arithmatic, (5) datapath and control, (6) micro-programming, (7) pipeling, (8)
storage hierarchy and virtual memory, (9) input/output, (10) different forms of parallelism including instruction level
parallelism, data-level parallelism using both vectors and message-passing multi-processors, and thread-level parallelism
using shared memory multiprocessors. Basic cache coherence and synchronization.
372. Computer Organization and Design Lab. (B) Corequisite(s): CSE 371. Laboratory for CSE 371. In this laboratory section, students gain experience
with digital design techniques by designing and implementing actual circuits using Verilog HDL and FPGAs. Five
assignments culminate in the design and simulation of a complete 16-bit integer pipelined CPU.
380. Computer Operating Systems. (A) Prerequisite(s): CSE 240 or EE 300. This course surveys methods and algorithms used in modern operating systems.
Concurrent distributed operation is emphasized. The main topics covered are as follows: process synchronization;
interprocess communication; concurrent/distributed programming languages; resource allocation and deadlock;
virtual memory; protection and security; distributed operation; distributed data; performance evalaution.
381. Computer Operating System Lab. (A) Corequisite(s): CSE 380. This course is a semester long project to design and implement your own operating
system. Typical components include a process management
system, a commond interpreter, and a file management
system.
390. (MEAM420, MEAM520) Machine Perception. (M) Prerequisite(s): MATH 240, PHYS 150 or MEAM 110/147. The rapidly evolving field of robotics includes systems designed to replace,
assist, or even entertain humans in a wide variety of tasks. Recent examples include planetary rovers, robotic pets, medical
surgical-assistive devices, and semiautonomous search-and-rescue vehicles. This introductory-level course presents
the fundamental kinematic, dynamic, and computational principles underlying most modern robotic systems. The main
topics of the course include: coordinate transformations, manipulator kinematics, mobile-robot kinematics,
actuation and sensing, feedback control, vision, motion planning, and learning. The material is reinforced with hands-on
lab exercises including basic robot- arm control and the programming of vision-guided mobile robots.
391. Introduction to Artificial Intelligence. (M) Prerequisite(s): CSE 121 and CSE 262; CSE 341 strongly recommended. Artificial Intelligence is considered from the point of view of a resource--limited
knowledge-based agent who must reason and act in the world. Topics include logic, automatic theorem proving,
search, knowledge representation and reasoning, natural language processing, probabilistic reasoning, and machine
learning. Programming assignments in Prolog and C++ or Java.
398. Quantum Computer and Information Science. (C) Prerequisite(s): CSE 260, 262 and Math 240. The purpose of this course is to introduce undergraduate students in computer
computer science and engineering to quantum computers (QC) and quantum information science (QIS). This course is
meant primarly for juniors and seniors in CSE. No prior knowledge of quantum mechanics (QM) is assumed. Enrollment
is by permission of the instructor.
400. Senior Project. (A) Prerequisite(s): Senior standing or permission of instructor. The goal of the senior design coruse is to provide students with an opportunity
to define, design and execute a significant project. Project subjects may revolve around software, hardware
or computational theory. Students must have an abstract of their Senior Project, which is approved and signed by a
Project Advisor early in the Fall semester. The project is expected to span two semesters; students must enroll in CSE 401
during the second semester. At the end of the first semester, students are required to submit an intermediate report
and give a presentation describing their project and progress. Grades are based on technical writing skills (as per submited
report) presentation skills and progress
on the project. These are evaluated by the
Project Adviser and the Course Instructor.
401. Senior Project. (B) Prerequisite(s): CSE 400, senior standing or permission of instructor. Continuation
of CSE 400. Design and implementation of a
significant piece of work: software, hardware
or theory. Students are required to submit
a final written report and give a final presentation
and demonstration of their project.
Grades are based on the report, the presentation and the satisfactory completion
of the project. These are evaluated by the Project Adviser and the Course Instructor.
410. (CIS 510) Curves and Surfaces: Theory and Applications. (M) The course introduces mathematical and algorithmic techniques for geometric
modeling with applications to computer graphics and
computer animation. The course covers implicit and
parametric curves; implicit and parametric surfaces;
polygonal surfaces; polygonal surface simplification,
decomposition, and parametrization; and surface reconstruction
from point sets.
430. Introduction to Human Language Technology. (A) Prerequisite(s): CIS 121. Automatic summarization can help alleviate the information
overload problem caused by the unprecedented amount
of online textual information. The building of a
summarization system requires good understanding
of the properties of human language and the use of
various natural language tools. In this course we
will build several summarization systems of increasing
complexity and sophistication. In the process we
will learn about various natural language processing
tools and resources such as part of speech tagging,
chunking, parsing, Wordnet, and machine learning toolkits. We will also cover probability and statistics concepts used in summarization,
but also applicable to a wide range of other language-related
tasks.
434. (CIS 534) Introduction to Parallel Processing. (C) This course is a pragmatic introduction to parallel and distributed programming.
It targets science and engineering students with
basic programming skills, and prepares them for parallelizing
existing sequential programs or optimizing the perfomrance
of existing parallel codes. The course teaches how
to program with widely used parallel programming
interfaces such as Pthread, MPI, OpenMP, HPFaand
RMI. In addition, the course covers enough information
on common parallel architectures, so that the students
can optimize the programs for different platforms.
455. (CIS 555) Internet and Web Systems. (C) Prerequisite(s): CIS 330, CIS 380 recommended. This course focuses on Internet
and Web technologies and the underlying principles
of distributed systems, information retrieval, and
data management. The material covered will include
web and application server architecturs, SML and semistructured data, schema mediation, document indexing and retrieval, peer-to-peer
systems, distributed transactions and remote procedure
calls. The course has a substantial group implementation
project.
460. (CIS 560) Computer Graphics. (A) Prerequisite(s): One year programming experience (C, JAVA, C++). A thorough
introduction to computer graphics techniques, covering
primarily 3D modeling and image synthesis. Topics
cover: geometric transformations, geometric algorithms,
software systems (OpenGL), 3D object models (surface and volume), visible surface algorithms, image synthesis, shading and
mapping, ray tracing, radiosity, global illumination,
photon mapping, anti-aliasing and compositing.
461. (CIS 561) Computer Modeling and Animation Applications. (C) Prerequisite(s): CSE 120, 121 or equivalent experience and concurrent or past enrollment in CSE 460/560. This
project-based course is designed to provide a comprehensive
introduction to the application of computer graphics
in a laboratory setting. Course materials and labs
will facilitate understanding issues and trends in
3D computer graphics. Students will develop a facility
with fundamental 3-D models and modeling software
through a series of projects. The course will offer
students a technical understanding of Polygonal and
Spline based modeling, alternative and standard methods
of 3-D model import and export, and model conversion.
It will also cover procedural and scripting methods,
techniques, and conventions for creating models and
shaders that will function properly for rendering
and animation. Practical application of topics covered
in CSE 460/560 include; geometric transformations,
hierarchies, articulation, modeling, blend shaps,
vertex weighting, and animation. Experiments with
various animation methods inlcude: dynamics, forward
and inverse kinematics, surface deformations, keyframe
interpolation, motion capture, procedural animation,
and facial animation. The course will be laboratory
based and will use industry standard software.
462. (CIS 562) Computer Animation. (C) Prerequisite(s): Previous exposure to major concepts in linear algebra (i.e. vector matrix math), curves and surfaces, dynamical systems (e.g. 2nd order
mass-spring-damper systems) and 3D computer graphics has also been assumed in the preparation of the course materials.
This course covers core subject matter common to
the fields of robotics, character animation and embodied
intelligent agents. The intent of the course is to
provide the student with a solid technical foundation
for developing, animating and controlling articulated
systems used in interactive computer games, virtual
reality simulations and high-end animation applications.
The course balances theory with practice by "looking
under the hood" of current animation systems
and authoring tools and examines the technolgies
and techniques used from both a computer science
and engineering perspective. Topics covered include:
geometric coordinate systems and transformations;
quaternions; parametric curves and surfaces; forward
and inverse kinematics; dynamic systems and control;
computer simulation; keyframe, motion capture and
procedural animation; behavior-based animation and
control; facial animation; smart characters and intelligent
agents.
477. (LING549) Mathematical Methods/Techniques for Linguistics and Natural Language
Processing. (M) Prerequisite(s): PHIL 006 or instructor's permission. Basic concepts of set theory, relations and functions, properties of relations.
Basic concepts of algebra. Grammars, languages, and automata- finite state grammars, regular expressions, context-free
and context-sensitive grammars, unrestricted grammars, finite automata, pushdown automata and other related
automata, Turing machines, Syntax and semantics of grammar formalisms. Strong generative capacity of grammars, Grammers
as deductive systems, parsing as deduction. Relevance of formal gammars to modeling biological sequences.
The course will deal with these topics in a very basic and introductory manner--ideas of proofs and not detailed proofs,
and more importantly with plenty of linguistic examples to bring out the linguistic relevance of these topics.
The course will deal with these topics in a very basic and introductory manner--ideas
of proofs and not detailed proofs, and more importantly
with plenty of linguistic examples to bring out the
linguistic relevance of these topics.
480. Real-Time and Embedded Systems. (M) Prerequisite(s): CIS 380, some network programming experience is desirable. Ever increasing availability of inexpensive processors connected
by a communication network has motivated the development
of numerous concepts and paradigms for distributed
real-time embedded systems. The primary objectives
of this course are to study the principles and concepts
of real-time embedded computing and to provide students
hands-on experience in developing embedded applications.
This course covers the concepts and theory necessary
to understand and program embedded real-time systems.
This includes concepts and theory for real-time system
design, analysis, and certification; programming
and operating systems for embedded systems; and concepts,
technologies, and protocols for distributed embedded
real-time systems.
The course will cover a variety of existing systems and technologies, e.g.,
real- machines, architectural description anguage,
formal meth and logical-time programming paradigms,
and certification The course requires active student
participation in-group projects. Each group will
be responsible for the design and implementation
of a life-critical embedded system such as a pacemaker.
The group projects are intended to complement the
learning of principles and concepts through the application
of theory in practice and the development of experimental
skills in building embedded applications.
482. (CIS 582) Logic In Computer Science. (C) Prerequisite(s): CSE 260. Logic has been called the calculus of computer science
as it plays a fundamental role in computer science,
similar to that played by calculus in the physical
sciences and traditional engineerng disciplines.
Indeed, logic is useful in areas of computer science
as disparate as architecture (logic gates), software
engineerng (specification and verification), programming
languages (semantics, logic programming), databases
(relational algebra and SQL), artificial intelligence
(automatic theorem proving), algorithms (complexity
and expressiveness), and theory of computation (general
notions of computability). CSE 482 provides the students
with a thorough introduction to mathematical logic,
covering in depth the topics of syntax, semantics,
decision procedures, formal proof systems, and soundness
and completeness for both propositional and first-order
logic. The material is taught froma computer science
perspective, with an emphasis on algorithms, computational complexity, and tools. Projects will focus on problems
in circuit design, specification and analysis and
protocols, and query evaluation in databases.
COMPUTER & INFORMATION SCIENCE (CIS)
Graduate Courses
500. Software Foundations. (C) Prerequisite(s): Undergraduate-level course in programming languages or compilers; significant programming experience. This course introduces basic
concepts and techniques in the foundational study
of programming languages. The central theme is the
view of individual programs and whole languages as
mathematical objects about which precise claims may
be made and proved. Particular topics include operational
techniques for formal definition of language features,
type systems and type safety properties, polymorphism
and subtyping, foundations of object-oriented programming,
and mechanisms supporting information hiding and
programming in the large.
L/R 501. Computer Architecture. (C) Prerequisite(s): Knowledge of computer organizaiton and basic programming skills. This course is an introductory graduate course on computer
architecture with an emphasis on a quantitative approach
to cost/performance design tradeoffs. The course
covers the fundamentals of classical and modern uniprocessor
design: performance and cost issues, instruction
sets, pipelining, superscalar, out-of-order, and
speculative execution mechanisms, caches, physical
memory, virtual memory, and I/O. Other topics include:
static scheduling, VLIW and EPIC, software speculation,
long (SIMD) and short (multimedia) vector execution,
multithreading, and an introduction to shared memory
multiprocessors.
502. Analysis of Algorithms. (C) Prerequisite(s): CIT 594 or equivalent. An investigation of several major algorithms and their uses in areas including
list manipulation, sorting, searching, selection and graph manipulation. Efficiency and complexity of algorithms. Compexity
Classes.
505. Software Systems. (C) Prerequisite(s): Undergraduate-level knowledge of Operating Systems and Networking, programming experience (CIT 594 or equivalent). This course provides an introduction to fundamental concepts of distributed
systems. Topics covered include communication, concurrency, programming paradigms, naming, managing shared state,
caching, syncronization, reaching agreement, fault tolerance, security, middleware, and distributed applications.
510. (CIS 410) Curves and Surfaces: Theory and Applications. (M) Prerequisite(s): Basic knowledge of linear algebra, calculus, and elementary geometry. CIS 560 is not required. The course is about mathematical and algorithmic techniques used for geometric
modeling and geometric design, using curves and surfaces. There are many applications in computer graphics as well
as in robotics, vision, and computational geometry. Such techniques are used in 2D and 3D drawing and plot,
object silhouettes, animating positions, product design (cars, planes, buidlings), topographic data, medical
imagery, active surfaces of proteins, attribute maps (color, texture, roughness), weather data, art, etc. Three broad
classes of problems will be considered: approximating curved shapes, using smooth curves or surfaces. Interpolating
curved shapes, using smooth curves or surfaces. Rendering smooth curves or surfaces.
511. Theory of Computation. (C) Prerequisite(s): Basic notions of discrete algebra. Finite automata (deterministic and nondeterministic) regular graphs, regular
expressions, regular grammars, (Nerode congruence), the "pumping lemma", closure properties. Context-free
languages. Standard forms: removal of e-rules, chain rules, reduced grammars. Chomsky Normal Form. Context-free languages as
fixed points (Ginsburg and Rose's Theorem). Greibach Normal Form (using Rosenkrantz's matrix method). Ogden's
Lemma and the "pumping lemma". Pushdown automata (PDA's). Equivalence of PDA's and context-free grammars. Brief
sketch of top-down and bottom- up (nondeterministic) parsing. Deterministic PDA's. Closure properties. Partial
recursive functions, Turing machines and RAM programs. Primitive recursion. Minimization. Equivalence of the models.
Church/Turing's thesis.
Acceptable Codings. A Universal RAM program. Undecidability of the halting problem.
Recursively enumerable sets (RE sets).
SM 518. (PHIL412) Topics in Logic; Finite Model Theory and Descriptive Complexity.
(C) This course will examine the expressive power of various logical languages over
the class of finite structures. The course begins
with an exposition of some of the fundamental theorems
about the behavior of first-order logic in the context
of finite structures, in particular, the Ehrenfeucht-Fraisse
Theorem and the Trahktenbrot Theorem. The first of
these results is used to show limitations on the
expressive power of first-order logic over finite
structures while the second result demonstrates that
the problem of reasoning about finite structures
using first-order logic is surprisingly complex.
The course then proceeds to consider various extensions
of first-order logic including fixed-point operators,
generalized quantifiers, infinitary languages, and
higher-order languages. The expressive power of these
extensions will be studied in detail and will be
connected to various problems in the theory of computational
complexity. This last motif, namely the relation
between descriptive and computational complexity,
will be one of the main themes of the course.
520.Machine Learning. (A) Prerequisite(s): Elementary probability, calculus, and linear algebra. Basic programming experience. This course covers the foundations of statistical machine learning. The focus
is on probabilistic and statistical methods for prediction and clustering in high dimensions. Other topics covered include
graphical models, dimensionality reduction, neural networks, and reinforcement learning. Fundamentals of AI. (C) Prerequisite(s): Students are expected to have the following background: Basic algorithms, data structures and complexity (dynamic programming, queues, stacks,
graphs, big-O, P/NP; Basic probability and statistics (random variables, standard distributions, simple
regression); Basic linear algebra (matrices, vectors, norms, inverses); Reasonable programming skills.Modern AI uses a collection of techniques from a number of fields in the design
of intelligent systems:probability, statistics, logic, operations research, optimal control and economics, to name
a few. This course covers basic modeling and algorithmic tools from these fields underlying current research and highlights
their applications in computer vision, robotics, and natural language processing.
530. Computational Linguistics. (A) Computational approaches to the problem of understanding and producing written
and spoken natural language, including speech processing,
syntactic parsing, statistical and corpus-based techniques,
semantic interpretation, discourse meaning, and the
role of pragmatics and world knowledge. It is recommended
that students have some knowledge of logic, basic
linguistics, and programming.
534. (CIS 434) Introduction to Parallel Processing. (C) This course is a pragmatic introduction to parallel programming. It is intended
for graduate students in computer science, as well
as all science and engineering students with an interest
in parallel programm. This course prepares the students
for parallelizing sequential progams or oprtimizing
the performance of existing parallel codes. After
a brief discussion of the basic notions of parallelism,
we will discuss several popular models of parallel
programming, including Pthreads, MPI, OpenMP, HPF,
Linda, and object-oriented parallel programming.
We will also study techniques to improve the efficiency
of parallel applications on various platforms. The
students are required to carry out a major programming
project that often originates from their discipline.
Assessment will be determined by some combination
of projects and exams.
L/L 535. (BIOL535, GCB 535) Introduction to Bioinformatics. (A) The course covers methods used in computational biology, including the statistical
models and algorithms used and the biological problems
which they address. Students will learn how tools
such as BLAST work, and will use them to address
real problems. The course will focus on sequence
analysis problems such as exon, motif and gene finding,
and on comparative methods but will also cover gene
expression and proteomics.
(BIOL536, GCB 536) Computational Biology. (A) Prerequisite(s): Math 104/114 or equivalent, BIOL 221 or equivalent, or permission of the instructor. Computational problems in molecular biology, including sequence search and analysis,
informatics, phylogenetic reconstruction, genetic mapping and optimization.
(BE 537) Biomedical Image Analysis. (C) Faculty. Prerequisite(s): Math through multivariate calculus (MATH 241), programming experience, as well as some familiarity with linear
algebra, basic physics, and statistics. This course covers the fundamentals of advanced quantitative image analysis
that apply to all of the major and emerging modalities in biological/biomaterials imaging and in vivo biomedical
imaging. While traditional image processing techniques will be discussed to provide context, the emphasis will
be on cutting edge aspects of all areas of image analysis (including registration, segmentation, and high-dimensional statistical
analysis). Significant coverage of state-of-the-art biomedical research and clinical applications will be incorporated
to reinforce the theoretical basis of the analysis methods.
550.Database and Information Systems. (A) Prerequisite(s): CIT 591 or equivalent. Introduction to the theory and practice of database management systems. The
Entity-Relationship approach as a modeling tool. The relational model, algebra and calculus. Commercial systems:
SQL, Quel and Ingres. Database design and relational normalization. Physical data organization and indexing
structures. Updates and integrity: transaction management, concurrency control and recovery techniques. Logic as
a data model: Datalog and evaluation techniques. The network model and object oriented approaches.
551.(TCOM551) Computer and Network Security. (B) Prerequisite(s): TCOM 512 or equivalent required; CIS 500 recommended. This is an introduction to topics in the security of computer systems and communication
on networks of computers. The course covers four major areas: fundamentals of cryptography, security for
communication protocols, security for operating systems and mobile programs, and security for electronic commerce.
Sample specific topics include: passwords and offline attacks, DES, RSA, DSA, SHA, SSL, CBC, IPSec, SET, DDoS
attacks, biometric authentication, PKI, smart cards, S/MIME, privacy on the Web, viruses, security models, wireless
security, and sandboxing. Students will be expected to display knowledge of both theory and practice through written
examinations and programming assignments.
553. (TCOM512) Networked Systems. (C) Prerequisite(s): CIS 121 (Programming Languages and Techniques II) or equivalent, or permission of the instructor. This course provides an introduction to fundamental concepts in the design and
implementation of networked systems, their protocols, and applications. Topics to be covered include: Internet architecture,
network applications, addressing, routing, quality of service, transport protocols, data link protocols, network
security, and application level protocols such as peer-to-peer networks and overlay networks. The course will involve
written assignments, examinations, and programming assignments.
555. (CIS 455) Internet and Web Systems. (C) Prerequisite(s): At least one year of Java programming. Database and operating systems familiarity recommended. This course focuses on the issues encountered in building Internet and web systems:
scalability, interoperability (of data and code), atomicity and consistency models, replication, and location
of resources, services, and data. Topics include other remote procedure calls, caching, replication, and hierarchical
structures; distributed consensus and transactions. Of particular focus will be techniques for locating machines,
resources, and data (including ranked web search, publish/subscribe systems, directories, and peer-to-peer protocols).
This course has a significant project-based component, in order to provide hands-onexperience with the ideas and algorithms
discussed. Students will construct and validate a large-scale distributed system, with some components developed
individually and some in teams.
558. (LING525) Computer Analysis and Modeling of Biological Signals and Systems.
(B) Prerequisite(s): Undergraduate-level knowledge of linear algebra. A graduate course intended to introduce the use of signal and image processing
tools for analyzing and modeling biological systems. We present a series of fundamental examples drawn from areas
of speech analysis/synthesis, computer vision, and modeling of biological perceptual systems. Students learn
the material through lectures and via a set of computer exercises developed in MATLAB.
560. (CSE 460) Computer Graphics. (A) Prerequisite(s): One year programming experience (C, JAVA, C++). A thorough introduction to computer graphics techniques, including 3D modeling,
rendering, and animation. Topics cover: geometric transformations, geometric algorithms, software systems (OpenGL),
3D object models (surface and volume), visible surface algorithms, image synthesis, shading and mapping, ray
tracing, radiosity, global illumination, photon mapping, anti-aliasing, animation techniques, and virtual environments.
562. (CIS 462) Computer Animation. (C) Prerequisite(s): Previous exposure to major concepts in linear algebra (i.e. vector matrix math), curves and surfaces, dynamical systems (e.g. 2nd order
mass-spring-damper systems) and 3D computer graphics has also been assumed in the preparation of the course materials. This course covers core subject matter common to the fields of robotics, character
animation and embodied intelligent agents. The intent of the course is to provide the student with a solid technical
foundation for developing, animating and controlling articulated systems used in interactive computer games, virtual
reality simulations and high-end animation applications. The course balances theory with practice by "looking
under the hood" of current animation systems and authoring tools and examines the technolgies and techniques used
from both a computer science and engineering perspective. Topics covered include: geometric coordinate systems
and transformations; quaternions; parametric curves and surfaces; forward and inverse kinematics; dynamic systems
and control; computer simulation; keyframe, motion capture and procedural animation; behavior-based animation
and control; facial animation; smart characters and intelligent agents.
563. Physically Based Animation. (C) Prerequisite(s): Prerequisite: CIS 460/560; CIS 462/562 or instructor's permission. Students should have a good knowledge of C++, OpenGL and basic familiarity
with linear algebra and physics. This course introduces students to common physically based modeling techniques
for animation of virtual characters, fluids and gases, rigid and deformable solids, cloth, explosions and other systems.
To gain hands-on experience, students implement basicsimulators for several systems. Topics include - Simulating
Deformable Objects: Particle Systems, Mass spring systems, Deformable Solids & Fracture, Cloth, Explosions & Fire,
Smoke, Fluids, Deformable active characters, Simulating Rigid bodies , Rigid bodies dynamics, Collision
detection and handling, Controlling rigid bodies simulation; Simulating Articulated Bodies: Simulated characters in games,
Optimization for character animation, Data driven approaches, Dynamic Response for Games. The course is
appropriate for both upper level undergraduate and graduate students.
564. Game Design and Development. (C) Basic understanding of 3D graphics and animation principles, priorexposure to scripting and programming languages such as Python, C and C++. The intent of the course is to provide students with a solid theoretical understanding
of the core creative principles, concepts, and game play structures/schemas underlying most game designs. The
course also will examine game development from an engineering point of view, including: game play mechanics,
game engine software and hardware architectures, user interfaces, design documents, playtesting and production
methods.
570. Modern Programming Language Implementation. (M) Prerequisite(s): CIS 500. An undergraduate course in compiler construction (CSE 341 or equivalent) is helpful but not required. This course is a broad introduction to advanced issues in compilers and run-time
systems for several classes of programming languages, including imperative, object-oriented, and functional.
Particular attention is paid to the structures, analyses, and transformations used in program optimization.
571. (PHIL411) Recursion Theory. (A) The course covers the basic theory of recursive and recursively enumerable sets
and the connection between this theory and a variety
of decision problems of interest in a computational
setting. The course will then proceed to an exposition
of recursion theoretic reducibilities. Elementary
results about degrees of unsolvability are established.
The theory of arithmetical, analytical, and projective
hierarchies will be presented. The study of functionals
at this point will provide an entry into the computationally
important subject of recursion at higher types. Basic
parts of the theory of inductive definitions and
monotone operators will be presented. If time and
interest permit, this theory will be applied to the
analysis of the semantical paradoxes. The course
will conclude with an investigation of the lower
levels of the analytical and projective hierarchies.
Applications to the degrees of unsolvability of various
logical systems will be presented, connections between
the hierarchies and predicative formal systems will
be established, and the relation between the theory
of the projective hierarchy and topics in classical
descriptive set theory will be indicated.
SM 572. (PHIL413) Set Theory. (C) This course is an introduction to set theory. It will begin with a study of
Zermelo-Fraenkel set theory (ZF) as a partial description
of the cumulative hierarchy of sets. Elementary properties
of cardinal and ordinal numbers will be developed
in ZF. The inner model of constructible sets will
be used to extablish the relative consistency of
the axiom of choice and the generalized continuum
hypothesis with ZF. The method of forcing will be
introduced to establish the independence of the continuum
hypothesis from ZF and other independence results.
Large cardinals and their bearing on the resolution
of questions about the continuum will be considered.
573. Software Engineering. (A) Prerequisite(s): CIT 591 and 593, or CIS 120, 121, and 240, or equivalent coursework; prior knowledge of Java required. BUILDING LARGE INFORMATION SYSTEMS:
This course will be a practicum in specifying, designing
and documenting, building, testing and administering
corporate-sized software projects that invariably
have a database component, security and firewall
issues, a web-based user interface, and special client
programs running as applications elsewhere on a network.
The course will examine one or more existing, commercial
systems (such as the Blackboard systemin use at Penn)
and will address conceptual issues surrounding large
software systems, such as ways to estimate project
size, and ways to integrate differenttechnologies
into a maintainable system design. There will be
substantial programming assignments using Microsoft
Visual Studio .NET languages such as C#to create
components of a larger system. Possible projects
include a web interface, a client (and possibly server-side
programming) for a SOAP/XML basedweb service, a Windows-based
client with graphical interface, and rudimentary
SQL database table and view design. The idea of using
UML for code generation, and writing "built-in" unit
tests for objects to automate future retesting willbe
examined.
580. Machine Perception. (A) Prerequisite(s): A solid grasp of the fundamentals of linear algebra. Some knowledge of programming in C and/or Matlab. An introduction to the problems
of computer vision and other forms of machine perception
that can be solved using geometrical approaches rather
than statistical methods. Emphasis will be placed
on both analytical and computational techniques.
This course is designed to provide students with
an exposure to the fundamental mathematical and algorithmic
techniques that are used to tackle challenging image
based modeling problems. The subject matter of this
course finds application in the fields of Computer
Vision, Computer Graphics and Robotics. Some of the
topics to be covered include: Projective Geometry,
Camera Calibration, Image Formation, Projective,
Affine and Euclidean Transformations, Computational
Stereopsis, and the recovery of 3D structure from
multiple 2D images. This course will also explore
various approaches to object recognition that make
use of geometric techniques, these would include
alignment based methods and techniques that exploit
geometric invariants. In the assignments for this
course, students will be able to apply the techniques
to actual computer vision problems.
582. (CIS 482) Logic in Computer Science. (C) Prerequisite(s): CIS 260 or CIT 592 or equivalent. Logic has been called the
calculus of computer science as it plays a fundamental
role in computer science, similar to that played
by calculus in the physical sciences and traditional
engineerng disciplines. Indeed, logic is useful in
areas of computer science as disparate as architecture
(logic gates), software engineerng (specification
and verification), programming languages (semantics,
logic programming), databases (relational algebra
and SQL), artificial intelligence (automatic theorem
proving), algorithms (complexity and expressiveness),
and theory of computation (general notions of computability).
CIS 582 provides the students with a thorough introduction
to mathematical logic, covering in depth the topics
of syntax, semantics, decision procedures, formal
proof systems, and soundness and completeness for
both propositional and first-order logic. The material
is taught froma computer science perspective, with
an emphasis on
algorithms, computational complexity, and tools. Projects will focus on problems
in circuit design, specification and analysis and
protocols, and query evaluation in databases.
610. (MATH676) Advanced Geometric Methods in Computer Science. (B) Prerequisite(s): CIS 510 or coverage of equivalent material. The purpose of this course is to present some of the advanced geometric methods
used in geometric modeling, computer graphics, computeer vision, etc. The topics may vary from year to year,
and will be selected among the following subjects (nonexhaustive list): Introduction to projective geometry
with applications to rational curves and surfaces, control points for rational curves, retangular and triangular rational
patches, drawing closed rational curves and surfaces; Differential geometry of curves (curvature, torsion, osculating
planes, the Frenet frame, osculating circles, osculating spheres); Differential geometry of surfaces (first fundamental
form, normal curvature, second fundamental form, geodesic curvature, Christoffel symbols, principal curvatures,
Gaussian curvature, mean curvature, the Gauss map and its derivative dN, the Dupin indicatrix, the Theorema Egregium
equations of Codazzi-Mainadi, Bonnet's theorem, lines of curvatures, geodesic torsion, asymptotic lines, geodesic
lines, local Gauss-Bonnet theorem).
613. (ESE 617, MEAM613) Nonlinear Control Theory. (M) Prerequisite(s): A sufficient background to linear algebra (ENM 510/511 or equivalent) and a course in linear control theory (MEAM
513 or equivalent), or written permission of the instructor.
The course studies issues in nonlinear control theory, with a particular emphasis
on the use of geometric principles. Topics include: controllability, accessibility, and observability, and observability
for nonlinear systems; Forbenius' theorem; feedback and input/outpub linearizaiton for SISO and MIMO systems;
dynamic extension; zero dynamics; output tracking and regulation; model matching disturbance decoupling; examples
will be taken from mechanical systems, robotic systems, including those involving nonholonomic constraints,
and active control of vibrations.
SM 620. Advanced Topics in Artificial Intelligence. (B) Prerequisite(s): CIS 520 or equivalent. Discussion of problems and techniques in Artificial Intelligence (AI): Knowledge
Representation, Natural Language Processing, Constraint Systems, Machine Learning; Applications of AI.
SM 630. Advanced Topics in Natural Language Processing. (C) Prerequisite(s): CIS 530 or equivalent or permission of instructor. Different topics selected each offering; e.g., NL generation, question-answering,
information extraction, machine translation, restricted grammar formalisms, computational lexical semantics,
etc.
SM 635. (BIOL537, GCB 537) Advanced Computational Biology. (A) Prerequisite(s): Biol 536 or permission of the instructor. Discussion of special research topics.
SM 639. Statistical approaches to Natural Language Understanding. (C) This course examines the recent development of corpus-based techniques in natural
language processing, focussing on both statistical and primarily symbolic learning techniques. Particular topics
vary from year to year.
SM 640. Advanced Topics in Software Systems. (B) Prerequisite(s): CIS 505 or equivalent. Different topics selected for each course offering.
SM 650. Advanced Topics in Databases. (B) Prerequisite(s): CIS 550. Advanced topics in databases: distributed databases, integrity constraints,
failure, concurrency control, relevant relational theory, semantics of data models, the interface between programming
of languages and databases. Object- oriented databases. New topics are discussed each year.
SM 660. Advanced Topics in Computer Graphics and Animation. (B) Prerequisite(s): CIS 560 or permission of the instructor. This course emphasizes the review and understanding of current
computer graphics, interaction, and virtual environment
research techniques and problems. Research-level
topics are based on recent ACM SIGGRAPH papers and
special effects techniques, through student-led discussions
and both oral and visual presentations. A software
project is required.
665. GPU Programming and Architecture. (C) Prerequisite(s): CIS 460 or CIS 560, and familiarity with computer hardware/systems.
The hardware/systems requirement may be met by CIS
501; or CIT 593 and 595; or CIS 240 (with CIS 371
recommended); or equivalent coursework.
This course examines the architecture and capabilities of modern GPUs. The graphics
processing unit (GPU) has grown in power over recent
years, to the point where many computations can be
performed faster on the GPU than on a traditional
CPU. GPUs have also become programmable, allowing
them to be used for a diverse set of applications
far removed from traditional graphics settings. Topics
covered include architectural aspects of modern GPUs,
with a special focus on their streaming parallel
nature, writing programs on the GPU using high level
languages like Cg and BrookGPU, and using the GPU
for graphicsand general purpose applications in the
area of geometry modelling, physical simulation,
scientific computing and games. Students are expected
to have a basic understanding of computer architecture
and graphics, and should be proficient in OpenGL
and C/C++.
SM 670. Advanced Topics in Programming Languages. (C) Prerequisite(s): CIS 500, or equivalent. The details of this course change
from year to year, but its purpose is to cover theoretical
topics related to programming languages. Some central
topics include: denotational vs operational semantics,
domain theory and category theory, the lambda calculus,
type theory (including recursive types, generics,
type inference and modules), logics of programs and
associated completeness and decidability problems,
specification languages, and models of concurrency.
The course requires a degree of mathematical sophistication.
673. Computer-Aided Verification. (C) Prerequisite(s): Basic knowledge of algorithms, data structures, automata theory,
propositional logic, operating systems, communication
protocols, and hardware (CSE 262, CSE 380, or permission
of the instructor).
This course introduces the theory and practice of formal methods for the design
and analysis of concurrent and embedded systems.
The emphasis is on the underlying logical and automata-theoretic
concepts, the algorithmic solutions, and heuristics
to cope with the high computational complexity. Topics:
Models and semantics of reactive systems; Verification
algorithms; Verification techniques. Topics may vary
depending on instructor.
677. Advanced Topics in Algorithms and Complexity. (A) Prerequisite(s): Consent of the instructor. This course covers various aspects
of discrete algorithms. Graph-theoretic algorithms
in computational biology, and randomization and computation; literature in dynamic graph algorithms, approximation
algorithms, and other areas according to student
interests.
SM 680. Advanced Topics in Machine Perception. (B) A previous course in machine perception or knowledge of image processing, experience
with an operating system and language such as Unix
and C, and aptitude for mathematics.
Graduate seminar in advanced work on machine perception as it applies to robots
as well as to the modelling of human perception.
Topics vary with each offering.
682. Friendly Logics. (C) The use of logical formalisms in Computer Science is dominated by a fundamentalconflict:
expressiveness vs. algorithmic tractability. Database
constraint logics, temporal logics and description
logics are successful compromises in this conflict:
(1) they are expressive enough for practical specifications
in certain areas, and (2) there exist interesting
algorithms for the automated useof these specifications.
Interesting connections can be made between these
logics because temporal and description logics are
modal logics, which in turn can be seen, as can database
constraint logics, as certain fragments of first-order
logic. These connections might benefit research in
databases, computer-aided verification and AI. Discussion
includes other interesting connections, eg., with
SLD-resolution, with constraint satisfaction problems,
with finite model theory and with automata theory.
700. Computer and Information Science Topics. (M) One time course offerings of special interest.
899. Independent Study. (C) For students studying a specific advanced subject area in computer and information
science. Involves coursework and clas presentations.
A CIS 899 course unit will invariably include formally
gradable work comparable to that in a normal 500
or 600 level course. This designation should not
be used for ongoing research towards a thesis, for
which the CIS 999 designation should be used.
990. Masters Thesis. For master's students who have taken ten couse units and need only to complete
the writing of a thesis or finish work for incompletes
in order to graduate. CIS 990 carries full time status
with zero course units and may be taken only once.
995. Dissertation. For Ph.D. candidates working exclusively on their dissertation research, having
completed 40 course units of credit.
996. Resesarch Seminar. (C) Introduction to research being conducted in the department. Mandatory for firstyear
doctoral students. Taken as fifth course for no credit
at no cost.
999. Thesis/Dissertation Research. (C) For students working on an advanced research program leading to the completion
of master's thesis or PhD dissertation requirements.
COMPUTER & INFORMATION TECHNOLOGY (CIT)
L/R 591. Programming Languages and Techniques I. (C) Introduction to fundamental concepts of programming and computer science. Principles
of modern object-oriented programming languages:
abstraction, types, polymorphism, encapsulation,
and inheritance. Basic algorithmic techniques and
informal complexity analysis. Substantial programming
assignments in Java.
L/R 592. Mathematical Foundations of Computer Science. (C) Foundations: Sets, Functions, Summations, and Sequences. Introduction to algorithms.
Counting techniques: The pigeonhole principle, permutations
and combinations. Discrete probability. Selected
topics from Number theory and/or Graph theory.
593. Introduction to Computer Architecture. (C) Introduction to fundamental concepts of computer architecture. Programming in
C and at least one assembly language as a basis for
understanding machine instructions and subroutine
linkage conventions. Representation of numbers, characters
and other information at machine level, including
on virtual machines. Features of current operating
systems.
594. Programming Languages and Techniques II. (C) Prerequisite(s): CIT 591 or consent of the instructor. Basic data structures,
including lists, stacks, queues, hash tables, trees,
priority queues, and Java Collections. Algorithms,
algorithm types, and simple complexity analysis.
Development and implementation of program specifications. Software architecture and design methods, including modular
program development, correctness arguments, and testing
techniques. Concepts illustrated through extensive
programming assignments in Java.
L/R 595. Digital System Organization and Design. (C) Prerequisite(s): CIT 593 or equivalent. Introduction to fundamental building blocks of digital computer hardware such
as transistors, logic gates and components built from them, as a basis for understanding how a computer operates
at the hardware level. Basic networking, security, and other "under the hood" topics. Use of virtual
machines to simulate hardware.
L/R 596. Theory of Computation. (C) Prerequisite(s): CIT 592 or equivalent. Relations. Finite automata, regular languages, regular grammars, and applications.
Pushdown automatia, trees, context-free grammars, and applications. Turing machines. Introduction to computability
and complexity theory.
597. Programming Languages and Techniques III. (C) Prerequisite(s): CIT 591 or equivalent. Advanced Java programming and programming
tools, with emphasis on developing for the Internet.
Java topics will include serialization, synchronization,
reflection, advanced I/O, and servlets. This course
will cover current Internet-related technologies such as XML and JavaScript, and may include JDBC,UML, PHP,
SOAP, and others. Substantial programming assignments,
many in Java. May be taken by MCIT and CIS graduate
students.
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