|
|
|
|
|
|
|
|
|
|
Course Criteria
Add courses to your favorites to save, share, and find your best transfer school.
-
3.00 Credits
09W: 10 10W: Arrange This course integrates discrete mathematics with algorithms and data structures, using computer science applications to motivate the mathematics. It covers logic and proof techniques, induction, set theory, counting, asymptotics, discrete probability, graphs, and trees. Mathematics 19 is identical to Computer Science 19 and may substitute for it in any requirement. Prerequisite: Computer Science 5, Engineering Sciences 20, or Advanced Placement. Dist: QDS. Zomorodian.
-
3.00 Credits
09W: 2 10W: Arrange This course presents topics related to interactive visual art generated on a computer. Although it briefly covers computer-generated media art, the course focuses on the programming skills required for creating interactive works. Rather than using commercial software, students write their own programs, using the Processing language, to create compositions with which users can interact. The course introduces fundamental concepts of how to represent and manipulate color, two-dimensional shapes, images, motion, and video. Coursework includes short programming assignments to practice the concepts introduced during lectures and projects to explore visual compositions. The course assumes no prior knowledge of programming. This course is not open to students who have passed Computer Science 5 or Engineering Sciences 20 or who have received credit for one of these courses via the Advanced Placement exam or the local placement exam. Dist: TLA. Bailey-Kellogg.
-
3.00 Credits
08F: 10A 09F: Arrange This projects-based lab course teaches the principles and practices of 3D modeling. Lectures focus on principles of modeling, materials, shading, and lighting. Students create a fully rigged character model while learning their way around a state-of-the-art 3D animation program. Assignments are given weekly. Students are graded on the successful completion of the projects, along with a midterm examination. Work will be evaluated on a set of technical and aesthetic criteria. Dist: TLA. Loeb.
-
3.00 Credits
09W: 12 09S: 10 10W, 10S: Arrange Techniques for building large, reliable, maintainable, and understandable software systems. Topics include UNIX tools and filters, programming in C, software testing, debugging, and teamwork in software development. Concepts are reinforced through a small number of medium-scale programs and one team programming project. Prerequisite: Computer Science 8. Dist: TLA. McDonald (winter), Campbell (spring).
-
3.00 Credits
08F: 10 09F: Arrange A survey of fundamental algorithms and algorithmic techniques, including divide-and-conquer algorithms, lower bounds, dynamic programming, greedy algorithms, amortized analysis, and graph algorithms. Presentation, implementation and formal analysis, including space/time complexity and proofs of correctness, are all emphasized. Prerequisite: Computer Science 8 and Computer Science 19. Dist: QDS. Jayanti.
-
3.00 Credits
08F, 09F: 12 A study and analysis of important numerical and computational methods for solving engineering and scientific problems. The course will include methods for solving linear and nonlinear equations, doing polynomial interpolation, evaluating integrals, solving ordinary differential equations, and determining eigenvalues and eigenvectors of matrices. The student will be required to write programs and run them on the computer. Prerequisite: Computer Science 5 and Mathematics 23. Dist: QDS. Shepherd.
-
3.00 Credits
09S: 2 10S: Arrange This course enables a student from another discipline to approach that discipline from a computational perspective-formulating computational problems, identifying suitable representations and approaches for solving them, and developing and implementing efficient solutions. The course assumes no computational background, and it introduces the fundamental computational skills that are useful in many disciplines. A series of laboratory exercises employ discrete, numerical, and statistical approaches to solve problems from a variety of disciplines. Solutions are developed in a high-level, interactive programming language that helps students learn and use the fundamental representations and techniques. Prerequisite: Mathematics 8. Open to students who have taken Computer Science 5. Dist: TLA. Choudhury.
-
3.00 Credits
09W: 11 10W: Arrange This hands-on course focuses on state-of-the-art computer animation, presenting techniques for traditional animation and how they apply to 3D computer animation, motion capture, and dynamic simulations. Facial and full-body animation are covered through projects, readings, and presentations, including physical simulation, procedural methods, image-based rendering, and machine-learning techniques. Students will create short animations. This course focuses on methods, ideas, and practical applications, rather than on mathematics. Dist: ART. Loeb.
-
3.00 Credits
09S: 10 Not offered every year This course studies the management of large bodies of data or information. This includes schemes for the representation, manipulation, and storage of complex information structures as well as algorithms for processing these structures efficiently and for retrieving the information they contain. This course will teach the student techniques for storage allocation and deallocation, retrieval (query formulation), and manipulation of large amounts of heterogeneous data. Students are expected to program and become involved in a project in which they study important aspects of a database system: ways to organize a distributed database shared by several computers; transactions that are processed locally and globally; robustness guarantees of the stored data against failure; security and data integrity guarantees from unauthorized access; privacy; object-oriented schemes for multimedia data; indexing, hashing, concurrency control, data mining, data warehousing, mobile databases and storage file structures. Prerequisite: Computer Science 23 or equivalent, as approved by instructor. Dist: TAS. Chakrabarti.
-
3.00 Credits
09S: 10A This course provides an introduction to statistical modeling and machine learning. Topics include learning theory, supervised and unsupervised machine learning, statistical inference and prediction, and data mining. Applications of these techniques to a wide variety of data sets will be described. Prerequisites: Computer Science 5, Computer Science 6, or Engineering Sciences 20; Mathematics 22 or 24. Dist: QDS. Torresani.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Privacy Statement
|
Terms of Use
|
Institutional Membership Information
|
About AcademyOne
Copyright 2006 - 2025 AcademyOne, Inc.
|
|
|