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.
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