CollegeTransfer.Net
Toggle menu
Home
Search
Search
Search Transfer Schools
Search for Course Equivalencies
Search for Exam Equivalencies
Search for Transfer Articulation Agreements
Search for Programs
Search for Courses
PA Bureau of CTE SOAR Programs
Transfer Student Center
Transfer Student Center
Adult Learners
Community College Students
High School Students
Traditional University Students
International Students
Military Learners and Veterans
About
About
Institutional information
Transfer FAQ
Register
Login
Course Criteria
Add courses to your favorites to save, share, and find your best transfer school.
COS 448: Innovating Across Technology, Business, and Marketplaces
0.00 - 4.00 Credits
Princeton University
This course introduces engineering students to the types of issues that are tackled by leading and innovative Chief Technology Officers: the technical visionaries and/or managers at companies who innovate at the boundaries of technology, business, and marketplaces by understanding all of these areas deeply. These individuals are true partners to the business leaders of the organization, not merely implementers of business goals. The focus will be on software technologies and businesses based on them. To use specific contexts, we will emphasize two complementary areas as examples: businesses based on cloud computing and on marketplaces.
Share
COS 448 - Innovating Across Technology, Business, and Marketplaces
Favorite
COS 451: Computational Geometry
0.00 - 4.00 Credits
Princeton University
This course introduces the basic concepts of geometric computing, illustrating the importance of this field for a variety of applications areas, such as computer graphics, solid modeling, robotics, database, pattern recognition, and statistical analysis. Algorithms are presented and analyzed for a large number of geometric problems, and an array of fundamental techniques are discussed (e.g., convex hulls, Voronoi diagrams, intersection problems, multidimensional searching).
Share
COS 451 - Computational Geometry
Favorite
COS 461: Computer Networks
0.00 - 4.00 Credits
Princeton University
This course studies computer networks and the services built on top of them. Topics include packet-switch and multi-access networks, routing and flow control, congestion control and quality-of-service, Internet protocols (IP, TCP, BGP), the client-server model and RPC, elements of distributed systems (naming, security, caching) and the design of network services (multimedia, file and web servers).
Share
COS 461 - Computer Networks
Favorite
COS 487: Theory of Computation
0.00 - 4.00 Credits
Princeton University
Studies the limits of computation by identifing tasks that are either inherently impossible to compute, or impossible to compute within the resources available. Introduces students to computability and decidability, Godel's incompleteness theorem, computational complexity, NP-completeness and other notions of intractability.This course also surveys the status of the P versus NP question. Additional topics may include: interactive proofs, hardness of computing approximate solutions, cryptography, and quantum computation. Two lectures, one precept. Prerequisite: COS 340/341 or instructor's permission.
Share
COS 487 - Theory of Computation
Favorite
COS 497: Senior Independent Work (B.S.E. candidates only)
0.00 - 4.00 Credits
Princeton University
Provides an opportunity for a student to concentrate on a "state-of-the-art" project in computer science. Topics may be selected from suggestions by faculty members or proposed by the student. The final choice must be approved by the faculty advisor.
Share
COS 497 - Senior Independent Work (B.S.E. candidates only)
Favorite
COS 513: Foundations of Probabilistic Modeling
0.00 - 4.00 Credits
Princeton University
Probabilistic modeling is a mainstay in machine learning research, providing essential tools for analyzing the vast amount of data that have become available in modern scientific research. Course studies probabilistic graphical models, a unifying formalism for describing and extending many previous methods from statistics and engineering; the mathematical foundations of this field; and the methods underlying the current state of the art. Prerequisites COS402 or COS424. Undergraduates by permission only.
Share
COS 513 - Foundations of Probabilistic Modeling
Favorite
COS 521: Advanced Algorithm Design
0.00 - 4.00 Credits
Princeton University
Advanced methods of algorithmic design and analysis: data structures, network flows, and linear programming. Solution of linear programs: Karmarkar and Ellipsoid algorithms. Probabilistic techniques. A selection of topics from on-line computation, approximation algorithms for NP-hard problems, number theoretic algorithms, geometric algorithms, and parallel computation.
Share
COS 521 - Advanced Algorithm Design
Favorite
COS 526: Advanced Computer Graphics
0.00 - 4.00 Credits
Princeton University
Advanced topics in computer graphics, with focus on learning recent methods in rendering, modeling, and animation. Appropriate for students who have taken COS426 (or equivalent) and who would like further exposure to computer graphics.
Share
COS 526 - Advanced Computer Graphics
Favorite
COS 551: Introduction to Genomics and Computational Molecular Biology
0.00 - 4.00 Credits
Princeton University
Introduction to computational and genomic approaches used to study molecular systems. Topics include computational approaches to sequence similarity and alignment, phylogenetic inference, gene expression analysis, structure prediction, comparative genome analysis, and high-throughput technologies for mapping genetic networks.
Share
COS 551 - Introduction to Genomics and Computational Molecular Biology
Favorite
COS 557: Analysis & Visualization of Large-Scale Genomic Data Sets
0.00 - 4.00 Credits
Princeton University
Introduces students to computational issues involved in analysis and display of large-scale biological data sets. Algorithms covered will include clustering and machine learning techniques for gene expression and proteomics data analysis, biological networks, joint learning from multiple data sources, and visualization issues for large-scale biological data sets. No prior knowledge of biology or bioinformatics is required; an introduction to bioinformatics and the nature of biological data will be provided. In depth knowledge of computer science is not required, but students should have some understanding of programming and computation.
Share
COS 557 - Analysis & Visualization of Large-Scale Genomic Data Sets
Favorite
First
Previous
41
42
43
44
45
Next
Last
Results Per Page:
10
20
30
40
50
Search Again
To find college, community college and university courses by keyword, enter some or all of the following, then select the Search button.
College:
(Type the name of a College, University, Exam, or Corporation)
Course Subject:
(For example: Accounting, Psychology)
Course Prefix and Number:
(For example: ACCT 101, where Course Prefix is ACCT, and Course Number is 101)
Course Title:
(For example: Introduction To Accounting)
Course Description:
(For example: Sine waves, Hemingway, or Impressionism)
Distance:
Within
5 miles
10 miles
25 miles
50 miles
100 miles
200 miles
of
Zip Code
Please enter a valid 5 or 9-digit Zip Code.
(For example: Find all institutions within 5 miles of the selected Zip Code)
State/Region:
Alabama
Alaska
American Samoa
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Federated States of Micronesia
Florida
Georgia
Guam
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Marshall Islands
Maryland
Massachusetts
Michigan
Minnesota
Minor Outlying Islands
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Northern Mariana Islands
Ohio
Oklahoma
Oregon
Palau
Pennsylvania
Puerto Rico
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virgin Islands
Virginia
Washington
West Virginia
Wisconsin
Wyoming
American Samoa
Guam
Northern Marianas Islands
Puerto Rico
Virgin Islands